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...
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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:	Multi-object tracking association affinity model sparse-based appearance affinity model rank-based motion affinity estimation

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 100780-100794
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:100780-100794

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2929182

Katalog-ID:	DOAJ020069855

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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
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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
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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
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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
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title	Multi-Pedestrian Tracking Based on Improved Two Step Data Association
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title_full	Multi-Pedestrian Tracking Based on Improved Two Step Data Association
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title_sort	multi-pedestrian tracking based on improved two step data association
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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.
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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
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