Multi-Pedestrian Tracking Based on Improved Two Step Data Association
The multi-object tracking (MOT) algorithms based on tracking by detection framework are the state-of-the-art trackers in recent years. Association optimization and association affinity model are two key parts in MOT, which have attracted attention to build effective association model to overcome amb...
Ausführliche Beschreibung
Autor*in: |
Honghong Yang [verfasserIn] Jingjing Li [verfasserIn] Jiahao Liu [verfasserIn] Yumei Zhang [verfasserIn] Xiaojun Wu [verfasserIn] Zhao Pei [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2019 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: IEEE Access - IEEE, 2014, 7(2019), Seite 100780-100794 |
---|---|
Übergeordnetes Werk: |
volume:7 ; year:2019 ; pages:100780-100794 |
Links: |
---|
DOI / URN: |
10.1109/ACCESS.2019.2929182 |
---|
Katalog-ID: |
DOAJ020069855 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ020069855 | ||
003 | DE-627 | ||
005 | 20230310114154.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230226s2019 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1109/ACCESS.2019.2929182 |2 doi | |
035 | |a (DE-627)DOAJ020069855 | ||
035 | |a (DE-599)DOAJbf91f0ab5c7d47e493bda91ebb5e01d8 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a TK1-9971 | |
100 | 0 | |a Honghong Yang |e verfasserin |4 aut | |
245 | 1 | 0 | |a Multi-Pedestrian Tracking Based on Improved Two Step Data Association |
264 | 1 | |c 2019 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a The multi-object tracking (MOT) algorithms based on tracking by detection framework are the state-of-the-art trackers in recent years. Association optimization and association affinity model are two key parts in MOT, which have attracted attention to build effective association model to overcome ambiguous detection responses. In this paper, we have proposed an online multi-pedestrian tracking algorithm that uses a two-step data association with the help of improved sparse based appearance affinity model and rank based motion affinity model. The association framework is constructed by fusing the trajectory dynamic information and confidence based two-step data associations. The missing frames of a tracklet are counted based on the sparse reconstruction error of a target. An incremental SVD and downdate SVD decomposition is devised to estimate the rank of the Hankel matrix in rank based motion model. The estimated result is fed back to compute the tracklets confidence during association optimization. Both those strategies are beneficial to eliminate ambiguous detection responses during association. By this association optimization strategy, the fragmented tracklets in online tracking are reduced in some extent. We evaluate our method on four public available challenging datasets. The experimental results, both qualitative and quantitative, demonstrate that the proposed tracking algorithm has a good tracking performance compared with several state-of-the-art multi-object trackers. | ||
650 | 4 | |a Multi-object tracking | |
650 | 4 | |a association affinity model | |
650 | 4 | |a sparse-based appearance affinity model | |
650 | 4 | |a rank-based motion affinity estimation | |
653 | 0 | |a Electrical engineering. Electronics. Nuclear engineering | |
700 | 0 | |a Jingjing Li |e verfasserin |4 aut | |
700 | 0 | |a Jiahao Liu |e verfasserin |4 aut | |
700 | 0 | |a Yumei Zhang |e verfasserin |4 aut | |
700 | 0 | |a Xiaojun Wu |e verfasserin |4 aut | |
700 | 0 | |a Zhao Pei |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t IEEE Access |d IEEE, 2014 |g 7(2019), Seite 100780-100794 |w (DE-627)728440385 |w (DE-600)2687964-5 |x 21693536 |7 nnns |
773 | 1 | 8 | |g volume:7 |g year:2019 |g pages:100780-100794 |
856 | 4 | 0 | |u https://doi.org/10.1109/ACCESS.2019.2929182 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/bf91f0ab5c7d47e493bda91ebb5e01d8 |z kostenfrei |
856 | 4 | 0 | |u https://ieeexplore.ieee.org/document/8764440/ |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2169-3536 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 7 |j 2019 |h 100780-100794 |
author_variant |
h y hy j l jl j l jl y z yz x w xw z p zp |
---|---|
matchkey_str |
article:21693536:2019----::utpdsrataknbsdnmrvdwse |
hierarchy_sort_str |
2019 |
callnumber-subject-code |
TK |
publishDate |
2019 |
allfields |
10.1109/ACCESS.2019.2929182 doi (DE-627)DOAJ020069855 (DE-599)DOAJbf91f0ab5c7d47e493bda91ebb5e01d8 DE-627 ger DE-627 rakwb eng TK1-9971 Honghong Yang verfasserin aut Multi-Pedestrian Tracking Based on Improved Two Step Data Association 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The multi-object tracking (MOT) algorithms based on tracking by detection framework are the state-of-the-art trackers in recent years. Association optimization and association affinity model are two key parts in MOT, which have attracted attention to build effective association model to overcome ambiguous detection responses. In this paper, we have proposed an online multi-pedestrian tracking algorithm that uses a two-step data association with the help of improved sparse based appearance affinity model and rank based motion affinity model. The association framework is constructed by fusing the trajectory dynamic information and confidence based two-step data associations. The missing frames of a tracklet are counted based on the sparse reconstruction error of a target. An incremental SVD and downdate SVD decomposition is devised to estimate the rank of the Hankel matrix in rank based motion model. The estimated result is fed back to compute the tracklets confidence during association optimization. Both those strategies are beneficial to eliminate ambiguous detection responses during association. By this association optimization strategy, the fragmented tracklets in online tracking are reduced in some extent. We evaluate our method on four public available challenging datasets. The experimental results, both qualitative and quantitative, demonstrate that the proposed tracking algorithm has a good tracking performance compared with several state-of-the-art multi-object trackers. Multi-object tracking association affinity model sparse-based appearance affinity model rank-based motion affinity estimation Electrical engineering. Electronics. Nuclear engineering Jingjing Li verfasserin aut Jiahao Liu verfasserin aut Yumei Zhang verfasserin aut Xiaojun Wu verfasserin aut Zhao Pei verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 100780-100794 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:100780-100794 https://doi.org/10.1109/ACCESS.2019.2929182 kostenfrei https://doaj.org/article/bf91f0ab5c7d47e493bda91ebb5e01d8 kostenfrei https://ieeexplore.ieee.org/document/8764440/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 100780-100794 |
spelling |
10.1109/ACCESS.2019.2929182 doi (DE-627)DOAJ020069855 (DE-599)DOAJbf91f0ab5c7d47e493bda91ebb5e01d8 DE-627 ger DE-627 rakwb eng TK1-9971 Honghong Yang verfasserin aut Multi-Pedestrian Tracking Based on Improved Two Step Data Association 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The multi-object tracking (MOT) algorithms based on tracking by detection framework are the state-of-the-art trackers in recent years. Association optimization and association affinity model are two key parts in MOT, which have attracted attention to build effective association model to overcome ambiguous detection responses. In this paper, we have proposed an online multi-pedestrian tracking algorithm that uses a two-step data association with the help of improved sparse based appearance affinity model and rank based motion affinity model. The association framework is constructed by fusing the trajectory dynamic information and confidence based two-step data associations. The missing frames of a tracklet are counted based on the sparse reconstruction error of a target. An incremental SVD and downdate SVD decomposition is devised to estimate the rank of the Hankel matrix in rank based motion model. The estimated result is fed back to compute the tracklets confidence during association optimization. Both those strategies are beneficial to eliminate ambiguous detection responses during association. By this association optimization strategy, the fragmented tracklets in online tracking are reduced in some extent. We evaluate our method on four public available challenging datasets. The experimental results, both qualitative and quantitative, demonstrate that the proposed tracking algorithm has a good tracking performance compared with several state-of-the-art multi-object trackers. Multi-object tracking association affinity model sparse-based appearance affinity model rank-based motion affinity estimation Electrical engineering. Electronics. Nuclear engineering Jingjing Li verfasserin aut Jiahao Liu verfasserin aut Yumei Zhang verfasserin aut Xiaojun Wu verfasserin aut Zhao Pei verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 100780-100794 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:100780-100794 https://doi.org/10.1109/ACCESS.2019.2929182 kostenfrei https://doaj.org/article/bf91f0ab5c7d47e493bda91ebb5e01d8 kostenfrei https://ieeexplore.ieee.org/document/8764440/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 100780-100794 |
allfields_unstemmed |
10.1109/ACCESS.2019.2929182 doi (DE-627)DOAJ020069855 (DE-599)DOAJbf91f0ab5c7d47e493bda91ebb5e01d8 DE-627 ger DE-627 rakwb eng TK1-9971 Honghong Yang verfasserin aut Multi-Pedestrian Tracking Based on Improved Two Step Data Association 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The multi-object tracking (MOT) algorithms based on tracking by detection framework are the state-of-the-art trackers in recent years. Association optimization and association affinity model are two key parts in MOT, which have attracted attention to build effective association model to overcome ambiguous detection responses. In this paper, we have proposed an online multi-pedestrian tracking algorithm that uses a two-step data association with the help of improved sparse based appearance affinity model and rank based motion affinity model. The association framework is constructed by fusing the trajectory dynamic information and confidence based two-step data associations. The missing frames of a tracklet are counted based on the sparse reconstruction error of a target. An incremental SVD and downdate SVD decomposition is devised to estimate the rank of the Hankel matrix in rank based motion model. The estimated result is fed back to compute the tracklets confidence during association optimization. Both those strategies are beneficial to eliminate ambiguous detection responses during association. By this association optimization strategy, the fragmented tracklets in online tracking are reduced in some extent. We evaluate our method on four public available challenging datasets. The experimental results, both qualitative and quantitative, demonstrate that the proposed tracking algorithm has a good tracking performance compared with several state-of-the-art multi-object trackers. Multi-object tracking association affinity model sparse-based appearance affinity model rank-based motion affinity estimation Electrical engineering. Electronics. Nuclear engineering Jingjing Li verfasserin aut Jiahao Liu verfasserin aut Yumei Zhang verfasserin aut Xiaojun Wu verfasserin aut Zhao Pei verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 100780-100794 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:100780-100794 https://doi.org/10.1109/ACCESS.2019.2929182 kostenfrei https://doaj.org/article/bf91f0ab5c7d47e493bda91ebb5e01d8 kostenfrei https://ieeexplore.ieee.org/document/8764440/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 100780-100794 |
allfieldsGer |
10.1109/ACCESS.2019.2929182 doi (DE-627)DOAJ020069855 (DE-599)DOAJbf91f0ab5c7d47e493bda91ebb5e01d8 DE-627 ger DE-627 rakwb eng TK1-9971 Honghong Yang verfasserin aut Multi-Pedestrian Tracking Based on Improved Two Step Data Association 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The multi-object tracking (MOT) algorithms based on tracking by detection framework are the state-of-the-art trackers in recent years. Association optimization and association affinity model are two key parts in MOT, which have attracted attention to build effective association model to overcome ambiguous detection responses. In this paper, we have proposed an online multi-pedestrian tracking algorithm that uses a two-step data association with the help of improved sparse based appearance affinity model and rank based motion affinity model. The association framework is constructed by fusing the trajectory dynamic information and confidence based two-step data associations. The missing frames of a tracklet are counted based on the sparse reconstruction error of a target. An incremental SVD and downdate SVD decomposition is devised to estimate the rank of the Hankel matrix in rank based motion model. The estimated result is fed back to compute the tracklets confidence during association optimization. Both those strategies are beneficial to eliminate ambiguous detection responses during association. By this association optimization strategy, the fragmented tracklets in online tracking are reduced in some extent. We evaluate our method on four public available challenging datasets. The experimental results, both qualitative and quantitative, demonstrate that the proposed tracking algorithm has a good tracking performance compared with several state-of-the-art multi-object trackers. Multi-object tracking association affinity model sparse-based appearance affinity model rank-based motion affinity estimation Electrical engineering. Electronics. Nuclear engineering Jingjing Li verfasserin aut Jiahao Liu verfasserin aut Yumei Zhang verfasserin aut Xiaojun Wu verfasserin aut Zhao Pei verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 100780-100794 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:100780-100794 https://doi.org/10.1109/ACCESS.2019.2929182 kostenfrei https://doaj.org/article/bf91f0ab5c7d47e493bda91ebb5e01d8 kostenfrei https://ieeexplore.ieee.org/document/8764440/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 100780-100794 |
allfieldsSound |
10.1109/ACCESS.2019.2929182 doi (DE-627)DOAJ020069855 (DE-599)DOAJbf91f0ab5c7d47e493bda91ebb5e01d8 DE-627 ger DE-627 rakwb eng TK1-9971 Honghong Yang verfasserin aut Multi-Pedestrian Tracking Based on Improved Two Step Data Association 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The multi-object tracking (MOT) algorithms based on tracking by detection framework are the state-of-the-art trackers in recent years. Association optimization and association affinity model are two key parts in MOT, which have attracted attention to build effective association model to overcome ambiguous detection responses. In this paper, we have proposed an online multi-pedestrian tracking algorithm that uses a two-step data association with the help of improved sparse based appearance affinity model and rank based motion affinity model. The association framework is constructed by fusing the trajectory dynamic information and confidence based two-step data associations. The missing frames of a tracklet are counted based on the sparse reconstruction error of a target. An incremental SVD and downdate SVD decomposition is devised to estimate the rank of the Hankel matrix in rank based motion model. The estimated result is fed back to compute the tracklets confidence during association optimization. Both those strategies are beneficial to eliminate ambiguous detection responses during association. By this association optimization strategy, the fragmented tracklets in online tracking are reduced in some extent. We evaluate our method on four public available challenging datasets. The experimental results, both qualitative and quantitative, demonstrate that the proposed tracking algorithm has a good tracking performance compared with several state-of-the-art multi-object trackers. Multi-object tracking association affinity model sparse-based appearance affinity model rank-based motion affinity estimation Electrical engineering. Electronics. Nuclear engineering Jingjing Li verfasserin aut Jiahao Liu verfasserin aut Yumei Zhang verfasserin aut Xiaojun Wu verfasserin aut Zhao Pei verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 100780-100794 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:100780-100794 https://doi.org/10.1109/ACCESS.2019.2929182 kostenfrei https://doaj.org/article/bf91f0ab5c7d47e493bda91ebb5e01d8 kostenfrei https://ieeexplore.ieee.org/document/8764440/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 100780-100794 |
language |
English |
source |
In IEEE Access 7(2019), Seite 100780-100794 volume:7 year:2019 pages:100780-100794 |
sourceStr |
In IEEE Access 7(2019), Seite 100780-100794 volume:7 year:2019 pages:100780-100794 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Multi-object tracking association affinity model sparse-based appearance affinity model rank-based motion affinity estimation Electrical engineering. Electronics. Nuclear engineering |
isfreeaccess_bool |
true |
container_title |
IEEE Access |
authorswithroles_txt_mv |
Honghong Yang @@aut@@ Jingjing Li @@aut@@ Jiahao Liu @@aut@@ Yumei Zhang @@aut@@ Xiaojun Wu @@aut@@ Zhao Pei @@aut@@ |
publishDateDaySort_date |
2019-01-01T00:00:00Z |
hierarchy_top_id |
728440385 |
id |
DOAJ020069855 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ020069855</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310114154.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2019.2929182</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ020069855</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJbf91f0ab5c7d47e493bda91ebb5e01d8</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Honghong Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multi-Pedestrian Tracking Based on Improved Two Step Data Association</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The multi-object tracking (MOT) algorithms based on tracking by detection framework are the state-of-the-art trackers in recent years. Association optimization and association affinity model are two key parts in MOT, which have attracted attention to build effective association model to overcome ambiguous detection responses. In this paper, we have proposed an online multi-pedestrian tracking algorithm that uses a two-step data association with the help of improved sparse based appearance affinity model and rank based motion affinity model. The association framework is constructed by fusing the trajectory dynamic information and confidence based two-step data associations. The missing frames of a tracklet are counted based on the sparse reconstruction error of a target. An incremental SVD and downdate SVD decomposition is devised to estimate the rank of the Hankel matrix in rank based motion model. The estimated result is fed back to compute the tracklets confidence during association optimization. Both those strategies are beneficial to eliminate ambiguous detection responses during association. By this association optimization strategy, the fragmented tracklets in online tracking are reduced in some extent. We evaluate our method on four public available challenging datasets. The experimental results, both qualitative and quantitative, demonstrate that the proposed tracking algorithm has a good tracking performance compared with several state-of-the-art multi-object trackers.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multi-object tracking</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">association affinity model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">sparse-based appearance affinity model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">rank-based motion affinity estimation</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jingjing Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jiahao Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yumei Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiaojun Wu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhao Pei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">7(2019), Seite 100780-100794</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:7</subfield><subfield code="g">year:2019</subfield><subfield code="g">pages:100780-100794</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2019.2929182</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/bf91f0ab5c7d47e493bda91ebb5e01d8</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/8764440/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">7</subfield><subfield code="j">2019</subfield><subfield code="h">100780-100794</subfield></datafield></record></collection>
|
callnumber-first |
T - Technology |
author |
Honghong Yang |
spellingShingle |
Honghong Yang misc TK1-9971 misc Multi-object tracking misc association affinity model misc sparse-based appearance affinity model misc rank-based motion affinity estimation misc Electrical engineering. Electronics. Nuclear engineering Multi-Pedestrian Tracking Based on Improved Two Step Data Association |
authorStr |
Honghong Yang |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)728440385 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
TK1-9971 |
illustrated |
Not Illustrated |
issn |
21693536 |
topic_title |
TK1-9971 Multi-Pedestrian Tracking Based on Improved Two Step Data Association Multi-object tracking association affinity model sparse-based appearance affinity model rank-based motion affinity estimation |
topic |
misc TK1-9971 misc Multi-object tracking misc association affinity model misc sparse-based appearance affinity model misc rank-based motion affinity estimation misc Electrical engineering. Electronics. Nuclear engineering |
topic_unstemmed |
misc TK1-9971 misc Multi-object tracking misc association affinity model misc sparse-based appearance affinity model misc rank-based motion affinity estimation misc Electrical engineering. Electronics. Nuclear engineering |
topic_browse |
misc TK1-9971 misc Multi-object tracking misc association affinity model misc sparse-based appearance affinity model misc rank-based motion affinity estimation misc Electrical engineering. Electronics. Nuclear engineering |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
IEEE Access |
hierarchy_parent_id |
728440385 |
hierarchy_top_title |
IEEE Access |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)728440385 (DE-600)2687964-5 |
title |
Multi-Pedestrian Tracking Based on Improved Two Step Data Association |
ctrlnum |
(DE-627)DOAJ020069855 (DE-599)DOAJbf91f0ab5c7d47e493bda91ebb5e01d8 |
title_full |
Multi-Pedestrian Tracking Based on Improved Two Step Data Association |
author_sort |
Honghong Yang |
journal |
IEEE Access |
journalStr |
IEEE Access |
callnumber-first-code |
T |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2019 |
contenttype_str_mv |
txt |
container_start_page |
100780 |
author_browse |
Honghong Yang Jingjing Li Jiahao Liu Yumei Zhang Xiaojun Wu Zhao Pei |
container_volume |
7 |
class |
TK1-9971 |
format_se |
Elektronische Aufsätze |
author-letter |
Honghong Yang |
doi_str_mv |
10.1109/ACCESS.2019.2929182 |
author2-role |
verfasserin |
title_sort |
multi-pedestrian tracking based on improved two step data association |
callnumber |
TK1-9971 |
title_auth |
Multi-Pedestrian Tracking Based on Improved Two Step Data Association |
abstract |
The multi-object tracking (MOT) algorithms based on tracking by detection framework are the state-of-the-art trackers in recent years. Association optimization and association affinity model are two key parts in MOT, which have attracted attention to build effective association model to overcome ambiguous detection responses. In this paper, we have proposed an online multi-pedestrian tracking algorithm that uses a two-step data association with the help of improved sparse based appearance affinity model and rank based motion affinity model. The association framework is constructed by fusing the trajectory dynamic information and confidence based two-step data associations. The missing frames of a tracklet are counted based on the sparse reconstruction error of a target. An incremental SVD and downdate SVD decomposition is devised to estimate the rank of the Hankel matrix in rank based motion model. The estimated result is fed back to compute the tracklets confidence during association optimization. Both those strategies are beneficial to eliminate ambiguous detection responses during association. By this association optimization strategy, the fragmented tracklets in online tracking are reduced in some extent. We evaluate our method on four public available challenging datasets. The experimental results, both qualitative and quantitative, demonstrate that the proposed tracking algorithm has a good tracking performance compared with several state-of-the-art multi-object trackers. |
abstractGer |
The multi-object tracking (MOT) algorithms based on tracking by detection framework are the state-of-the-art trackers in recent years. Association optimization and association affinity model are two key parts in MOT, which have attracted attention to build effective association model to overcome ambiguous detection responses. In this paper, we have proposed an online multi-pedestrian tracking algorithm that uses a two-step data association with the help of improved sparse based appearance affinity model and rank based motion affinity model. The association framework is constructed by fusing the trajectory dynamic information and confidence based two-step data associations. The missing frames of a tracklet are counted based on the sparse reconstruction error of a target. An incremental SVD and downdate SVD decomposition is devised to estimate the rank of the Hankel matrix in rank based motion model. The estimated result is fed back to compute the tracklets confidence during association optimization. Both those strategies are beneficial to eliminate ambiguous detection responses during association. By this association optimization strategy, the fragmented tracklets in online tracking are reduced in some extent. We evaluate our method on four public available challenging datasets. The experimental results, both qualitative and quantitative, demonstrate that the proposed tracking algorithm has a good tracking performance compared with several state-of-the-art multi-object trackers. |
abstract_unstemmed |
The multi-object tracking (MOT) algorithms based on tracking by detection framework are the state-of-the-art trackers in recent years. Association optimization and association affinity model are two key parts in MOT, which have attracted attention to build effective association model to overcome ambiguous detection responses. In this paper, we have proposed an online multi-pedestrian tracking algorithm that uses a two-step data association with the help of improved sparse based appearance affinity model and rank based motion affinity model. The association framework is constructed by fusing the trajectory dynamic information and confidence based two-step data associations. The missing frames of a tracklet are counted based on the sparse reconstruction error of a target. An incremental SVD and downdate SVD decomposition is devised to estimate the rank of the Hankel matrix in rank based motion model. The estimated result is fed back to compute the tracklets confidence during association optimization. Both those strategies are beneficial to eliminate ambiguous detection responses during association. By this association optimization strategy, the fragmented tracklets in online tracking are reduced in some extent. We evaluate our method on four public available challenging datasets. The experimental results, both qualitative and quantitative, demonstrate that the proposed tracking algorithm has a good tracking performance compared with several state-of-the-art multi-object trackers. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
title_short |
Multi-Pedestrian Tracking Based on Improved Two Step Data Association |
url |
https://doi.org/10.1109/ACCESS.2019.2929182 https://doaj.org/article/bf91f0ab5c7d47e493bda91ebb5e01d8 https://ieeexplore.ieee.org/document/8764440/ https://doaj.org/toc/2169-3536 |
remote_bool |
true |
author2 |
Jingjing Li Jiahao Liu Yumei Zhang Xiaojun Wu Zhao Pei |
author2Str |
Jingjing Li Jiahao Liu Yumei Zhang Xiaojun Wu Zhao Pei |
ppnlink |
728440385 |
callnumber-subject |
TK - Electrical and Nuclear Engineering |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.1109/ACCESS.2019.2929182 |
callnumber-a |
TK1-9971 |
up_date |
2024-07-04T01:59:13.974Z |
_version_ |
1803611908578738176 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ020069855</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310114154.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2019.2929182</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ020069855</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJbf91f0ab5c7d47e493bda91ebb5e01d8</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Honghong Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multi-Pedestrian Tracking Based on Improved Two Step Data Association</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The multi-object tracking (MOT) algorithms based on tracking by detection framework are the state-of-the-art trackers in recent years. Association optimization and association affinity model are two key parts in MOT, which have attracted attention to build effective association model to overcome ambiguous detection responses. In this paper, we have proposed an online multi-pedestrian tracking algorithm that uses a two-step data association with the help of improved sparse based appearance affinity model and rank based motion affinity model. The association framework is constructed by fusing the trajectory dynamic information and confidence based two-step data associations. The missing frames of a tracklet are counted based on the sparse reconstruction error of a target. An incremental SVD and downdate SVD decomposition is devised to estimate the rank of the Hankel matrix in rank based motion model. The estimated result is fed back to compute the tracklets confidence during association optimization. Both those strategies are beneficial to eliminate ambiguous detection responses during association. By this association optimization strategy, the fragmented tracklets in online tracking are reduced in some extent. We evaluate our method on four public available challenging datasets. The experimental results, both qualitative and quantitative, demonstrate that the proposed tracking algorithm has a good tracking performance compared with several state-of-the-art multi-object trackers.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multi-object tracking</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">association affinity model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">sparse-based appearance affinity model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">rank-based motion affinity estimation</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jingjing Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jiahao Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yumei Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiaojun Wu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhao Pei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">7(2019), Seite 100780-100794</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:7</subfield><subfield code="g">year:2019</subfield><subfield code="g">pages:100780-100794</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2019.2929182</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/bf91f0ab5c7d47e493bda91ebb5e01d8</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/8764440/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">7</subfield><subfield code="j">2019</subfield><subfield code="h">100780-100794</subfield></datafield></record></collection>
|
score |
7.3995275 |