Action recognition in poor-quality spectator crowd videos using head distribution-based person segmentation
Abstract Despite a big volume of research on action recognition, little attention has been given to individual action recognition in poor-quality spectator crowd scenes. It is an important scenario, because most of the surveillance systems generate poor-quality videos, though current state-of-the-ar...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mahmood, Arif [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Machine vision and applications - Berlin : Springer, 1988, 30(2019), 6 vom: 15. Juni, Seite 1083-1096 |
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| Übergeordnetes Werk: |
volume:30 ; year:2019 ; number:6 ; day:15 ; month:06 ; pages:1083-1096 |
| Links: |
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| DOI / URN: |
10.1007/s00138-019-01039-3 |
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SPR001259644 |
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| 100 | 1 | |a Mahmood, Arif |e verfasserin |0 (orcid)0000-0001-5986-9876 |4 aut | |
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| 520 | |a Abstract Despite a big volume of research on action recognition, little attention has been given to individual action recognition in poor-quality spectator crowd scenes. It is an important scenario, because most of the surveillance systems generate poor-quality videos, though current state-of-the-art methods may not be effectively applicable. Therefore recognizing actions performed by individuals in poor-quality spectator crowd scenes is an unsolved problem. In such cases, the main challenge is localizing person proposals for each actor in the crowd. This challenge becomes more difficult when occlusion is severe. In this work, we propose a novel approach to find person proposals in poor-quality spectator crowds using crowd-based constraints. First, we define persons in the crowd by using efficient person head detectors. We exploit person head size to estimate the person bounding box using linear regression. Then, we use distribution of heads in the crowd image to estimate more accurate person proposals. Motion trajectories are independently computed in the video without considering persons and then assigned to each person based on a novel distance measure computed between the trajectory and the person proposal. The set of trajectories and associated motion and texture-based features in overlapped time windows are used to compute the final feature vector. For each time window using early information fusion in the bag of visual-words framework, cumulative feature vectors are computed encoding action information. Experiments are performed on a publicly available real-world spectator crowd dataset containing as many as 150 actors performing multiple actions at the same time. Our experiments have demonstrated excellent performance of the proposed technique. | ||
| 650 | 4 | |a Crowd analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Action detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Low resolution surveillance |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Crowd action detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Crowd security |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Crowd surveillance |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Al-Maadeed, Somaya |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Machine vision and applications |d Berlin : Springer, 1988 |g 30(2019), 6 vom: 15. Juni, Seite 1083-1096 |w (DE-627)300186312 |w (DE-600)1481698-2 |x 1432-1769 |7 nnns |
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10.1007/s00138-019-01039-3 doi (DE-627)SPR001259644 (SPR)s00138-019-01039-3-e DE-627 ger DE-627 rakwb eng Mahmood, Arif verfasserin (orcid)0000-0001-5986-9876 aut Action recognition in poor-quality spectator crowd videos using head distribution-based person segmentation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Despite a big volume of research on action recognition, little attention has been given to individual action recognition in poor-quality spectator crowd scenes. It is an important scenario, because most of the surveillance systems generate poor-quality videos, though current state-of-the-art methods may not be effectively applicable. Therefore recognizing actions performed by individuals in poor-quality spectator crowd scenes is an unsolved problem. In such cases, the main challenge is localizing person proposals for each actor in the crowd. This challenge becomes more difficult when occlusion is severe. In this work, we propose a novel approach to find person proposals in poor-quality spectator crowds using crowd-based constraints. First, we define persons in the crowd by using efficient person head detectors. We exploit person head size to estimate the person bounding box using linear regression. Then, we use distribution of heads in the crowd image to estimate more accurate person proposals. Motion trajectories are independently computed in the video without considering persons and then assigned to each person based on a novel distance measure computed between the trajectory and the person proposal. The set of trajectories and associated motion and texture-based features in overlapped time windows are used to compute the final feature vector. For each time window using early information fusion in the bag of visual-words framework, cumulative feature vectors are computed encoding action information. Experiments are performed on a publicly available real-world spectator crowd dataset containing as many as 150 actors performing multiple actions at the same time. Our experiments have demonstrated excellent performance of the proposed technique. Crowd analysis (dpeaa)DE-He213 Action detection (dpeaa)DE-He213 Low resolution surveillance (dpeaa)DE-He213 Crowd action detection (dpeaa)DE-He213 Crowd security (dpeaa)DE-He213 Crowd surveillance (dpeaa)DE-He213 Al-Maadeed, Somaya aut Enthalten in Machine vision and applications Berlin : Springer, 1988 30(2019), 6 vom: 15. Juni, Seite 1083-1096 (DE-627)300186312 (DE-600)1481698-2 1432-1769 nnns volume:30 year:2019 number:6 day:15 month:06 pages:1083-1096 https://dx.doi.org/10.1007/s00138-019-01039-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 30 2019 6 15 06 1083-1096 |
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10.1007/s00138-019-01039-3 doi (DE-627)SPR001259644 (SPR)s00138-019-01039-3-e DE-627 ger DE-627 rakwb eng Mahmood, Arif verfasserin (orcid)0000-0001-5986-9876 aut Action recognition in poor-quality spectator crowd videos using head distribution-based person segmentation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Despite a big volume of research on action recognition, little attention has been given to individual action recognition in poor-quality spectator crowd scenes. It is an important scenario, because most of the surveillance systems generate poor-quality videos, though current state-of-the-art methods may not be effectively applicable. Therefore recognizing actions performed by individuals in poor-quality spectator crowd scenes is an unsolved problem. In such cases, the main challenge is localizing person proposals for each actor in the crowd. This challenge becomes more difficult when occlusion is severe. In this work, we propose a novel approach to find person proposals in poor-quality spectator crowds using crowd-based constraints. First, we define persons in the crowd by using efficient person head detectors. We exploit person head size to estimate the person bounding box using linear regression. Then, we use distribution of heads in the crowd image to estimate more accurate person proposals. Motion trajectories are independently computed in the video without considering persons and then assigned to each person based on a novel distance measure computed between the trajectory and the person proposal. The set of trajectories and associated motion and texture-based features in overlapped time windows are used to compute the final feature vector. For each time window using early information fusion in the bag of visual-words framework, cumulative feature vectors are computed encoding action information. Experiments are performed on a publicly available real-world spectator crowd dataset containing as many as 150 actors performing multiple actions at the same time. Our experiments have demonstrated excellent performance of the proposed technique. Crowd analysis (dpeaa)DE-He213 Action detection (dpeaa)DE-He213 Low resolution surveillance (dpeaa)DE-He213 Crowd action detection (dpeaa)DE-He213 Crowd security (dpeaa)DE-He213 Crowd surveillance (dpeaa)DE-He213 Al-Maadeed, Somaya aut Enthalten in Machine vision and applications Berlin : Springer, 1988 30(2019), 6 vom: 15. Juni, Seite 1083-1096 (DE-627)300186312 (DE-600)1481698-2 1432-1769 nnns volume:30 year:2019 number:6 day:15 month:06 pages:1083-1096 https://dx.doi.org/10.1007/s00138-019-01039-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 30 2019 6 15 06 1083-1096 |
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10.1007/s00138-019-01039-3 doi (DE-627)SPR001259644 (SPR)s00138-019-01039-3-e DE-627 ger DE-627 rakwb eng Mahmood, Arif verfasserin (orcid)0000-0001-5986-9876 aut Action recognition in poor-quality spectator crowd videos using head distribution-based person segmentation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Despite a big volume of research on action recognition, little attention has been given to individual action recognition in poor-quality spectator crowd scenes. It is an important scenario, because most of the surveillance systems generate poor-quality videos, though current state-of-the-art methods may not be effectively applicable. Therefore recognizing actions performed by individuals in poor-quality spectator crowd scenes is an unsolved problem. In such cases, the main challenge is localizing person proposals for each actor in the crowd. This challenge becomes more difficult when occlusion is severe. In this work, we propose a novel approach to find person proposals in poor-quality spectator crowds using crowd-based constraints. First, we define persons in the crowd by using efficient person head detectors. We exploit person head size to estimate the person bounding box using linear regression. Then, we use distribution of heads in the crowd image to estimate more accurate person proposals. Motion trajectories are independently computed in the video without considering persons and then assigned to each person based on a novel distance measure computed between the trajectory and the person proposal. The set of trajectories and associated motion and texture-based features in overlapped time windows are used to compute the final feature vector. For each time window using early information fusion in the bag of visual-words framework, cumulative feature vectors are computed encoding action information. Experiments are performed on a publicly available real-world spectator crowd dataset containing as many as 150 actors performing multiple actions at the same time. Our experiments have demonstrated excellent performance of the proposed technique. Crowd analysis (dpeaa)DE-He213 Action detection (dpeaa)DE-He213 Low resolution surveillance (dpeaa)DE-He213 Crowd action detection (dpeaa)DE-He213 Crowd security (dpeaa)DE-He213 Crowd surveillance (dpeaa)DE-He213 Al-Maadeed, Somaya aut Enthalten in Machine vision and applications Berlin : Springer, 1988 30(2019), 6 vom: 15. Juni, Seite 1083-1096 (DE-627)300186312 (DE-600)1481698-2 1432-1769 nnns volume:30 year:2019 number:6 day:15 month:06 pages:1083-1096 https://dx.doi.org/10.1007/s00138-019-01039-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 30 2019 6 15 06 1083-1096 |
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10.1007/s00138-019-01039-3 doi (DE-627)SPR001259644 (SPR)s00138-019-01039-3-e DE-627 ger DE-627 rakwb eng Mahmood, Arif verfasserin (orcid)0000-0001-5986-9876 aut Action recognition in poor-quality spectator crowd videos using head distribution-based person segmentation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Despite a big volume of research on action recognition, little attention has been given to individual action recognition in poor-quality spectator crowd scenes. It is an important scenario, because most of the surveillance systems generate poor-quality videos, though current state-of-the-art methods may not be effectively applicable. Therefore recognizing actions performed by individuals in poor-quality spectator crowd scenes is an unsolved problem. In such cases, the main challenge is localizing person proposals for each actor in the crowd. This challenge becomes more difficult when occlusion is severe. In this work, we propose a novel approach to find person proposals in poor-quality spectator crowds using crowd-based constraints. First, we define persons in the crowd by using efficient person head detectors. We exploit person head size to estimate the person bounding box using linear regression. Then, we use distribution of heads in the crowd image to estimate more accurate person proposals. Motion trajectories are independently computed in the video without considering persons and then assigned to each person based on a novel distance measure computed between the trajectory and the person proposal. The set of trajectories and associated motion and texture-based features in overlapped time windows are used to compute the final feature vector. For each time window using early information fusion in the bag of visual-words framework, cumulative feature vectors are computed encoding action information. Experiments are performed on a publicly available real-world spectator crowd dataset containing as many as 150 actors performing multiple actions at the same time. Our experiments have demonstrated excellent performance of the proposed technique. Crowd analysis (dpeaa)DE-He213 Action detection (dpeaa)DE-He213 Low resolution surveillance (dpeaa)DE-He213 Crowd action detection (dpeaa)DE-He213 Crowd security (dpeaa)DE-He213 Crowd surveillance (dpeaa)DE-He213 Al-Maadeed, Somaya aut Enthalten in Machine vision and applications Berlin : Springer, 1988 30(2019), 6 vom: 15. Juni, Seite 1083-1096 (DE-627)300186312 (DE-600)1481698-2 1432-1769 nnns volume:30 year:2019 number:6 day:15 month:06 pages:1083-1096 https://dx.doi.org/10.1007/s00138-019-01039-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 30 2019 6 15 06 1083-1096 |
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10.1007/s00138-019-01039-3 doi (DE-627)SPR001259644 (SPR)s00138-019-01039-3-e DE-627 ger DE-627 rakwb eng Mahmood, Arif verfasserin (orcid)0000-0001-5986-9876 aut Action recognition in poor-quality spectator crowd videos using head distribution-based person segmentation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Despite a big volume of research on action recognition, little attention has been given to individual action recognition in poor-quality spectator crowd scenes. It is an important scenario, because most of the surveillance systems generate poor-quality videos, though current state-of-the-art methods may not be effectively applicable. Therefore recognizing actions performed by individuals in poor-quality spectator crowd scenes is an unsolved problem. In such cases, the main challenge is localizing person proposals for each actor in the crowd. This challenge becomes more difficult when occlusion is severe. In this work, we propose a novel approach to find person proposals in poor-quality spectator crowds using crowd-based constraints. First, we define persons in the crowd by using efficient person head detectors. We exploit person head size to estimate the person bounding box using linear regression. Then, we use distribution of heads in the crowd image to estimate more accurate person proposals. Motion trajectories are independently computed in the video without considering persons and then assigned to each person based on a novel distance measure computed between the trajectory and the person proposal. The set of trajectories and associated motion and texture-based features in overlapped time windows are used to compute the final feature vector. For each time window using early information fusion in the bag of visual-words framework, cumulative feature vectors are computed encoding action information. Experiments are performed on a publicly available real-world spectator crowd dataset containing as many as 150 actors performing multiple actions at the same time. Our experiments have demonstrated excellent performance of the proposed technique. Crowd analysis (dpeaa)DE-He213 Action detection (dpeaa)DE-He213 Low resolution surveillance (dpeaa)DE-He213 Crowd action detection (dpeaa)DE-He213 Crowd security (dpeaa)DE-He213 Crowd surveillance (dpeaa)DE-He213 Al-Maadeed, Somaya aut Enthalten in Machine vision and applications Berlin : Springer, 1988 30(2019), 6 vom: 15. Juni, Seite 1083-1096 (DE-627)300186312 (DE-600)1481698-2 1432-1769 nnns volume:30 year:2019 number:6 day:15 month:06 pages:1083-1096 https://dx.doi.org/10.1007/s00138-019-01039-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 30 2019 6 15 06 1083-1096 |
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Mahmood, Arif |
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Mahmood, Arif misc Crowd analysis misc Action detection misc Low resolution surveillance misc Crowd action detection misc Crowd security misc Crowd surveillance Action recognition in poor-quality spectator crowd videos using head distribution-based person segmentation |
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Action recognition in poor-quality spectator crowd videos using head distribution-based person segmentation Crowd analysis (dpeaa)DE-He213 Action detection (dpeaa)DE-He213 Low resolution surveillance (dpeaa)DE-He213 Crowd action detection (dpeaa)DE-He213 Crowd security (dpeaa)DE-He213 Crowd surveillance (dpeaa)DE-He213 |
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action recognition in poor-quality spectator crowd videos using head distribution-based person segmentation |
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Action recognition in poor-quality spectator crowd videos using head distribution-based person segmentation |
| abstract |
Abstract Despite a big volume of research on action recognition, little attention has been given to individual action recognition in poor-quality spectator crowd scenes. It is an important scenario, because most of the surveillance systems generate poor-quality videos, though current state-of-the-art methods may not be effectively applicable. Therefore recognizing actions performed by individuals in poor-quality spectator crowd scenes is an unsolved problem. In such cases, the main challenge is localizing person proposals for each actor in the crowd. This challenge becomes more difficult when occlusion is severe. In this work, we propose a novel approach to find person proposals in poor-quality spectator crowds using crowd-based constraints. First, we define persons in the crowd by using efficient person head detectors. We exploit person head size to estimate the person bounding box using linear regression. Then, we use distribution of heads in the crowd image to estimate more accurate person proposals. Motion trajectories are independently computed in the video without considering persons and then assigned to each person based on a novel distance measure computed between the trajectory and the person proposal. The set of trajectories and associated motion and texture-based features in overlapped time windows are used to compute the final feature vector. For each time window using early information fusion in the bag of visual-words framework, cumulative feature vectors are computed encoding action information. Experiments are performed on a publicly available real-world spectator crowd dataset containing as many as 150 actors performing multiple actions at the same time. Our experiments have demonstrated excellent performance of the proposed technique. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
| abstractGer |
Abstract Despite a big volume of research on action recognition, little attention has been given to individual action recognition in poor-quality spectator crowd scenes. It is an important scenario, because most of the surveillance systems generate poor-quality videos, though current state-of-the-art methods may not be effectively applicable. Therefore recognizing actions performed by individuals in poor-quality spectator crowd scenes is an unsolved problem. In such cases, the main challenge is localizing person proposals for each actor in the crowd. This challenge becomes more difficult when occlusion is severe. In this work, we propose a novel approach to find person proposals in poor-quality spectator crowds using crowd-based constraints. First, we define persons in the crowd by using efficient person head detectors. We exploit person head size to estimate the person bounding box using linear regression. Then, we use distribution of heads in the crowd image to estimate more accurate person proposals. Motion trajectories are independently computed in the video without considering persons and then assigned to each person based on a novel distance measure computed between the trajectory and the person proposal. The set of trajectories and associated motion and texture-based features in overlapped time windows are used to compute the final feature vector. For each time window using early information fusion in the bag of visual-words framework, cumulative feature vectors are computed encoding action information. Experiments are performed on a publicly available real-world spectator crowd dataset containing as many as 150 actors performing multiple actions at the same time. Our experiments have demonstrated excellent performance of the proposed technique. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract Despite a big volume of research on action recognition, little attention has been given to individual action recognition in poor-quality spectator crowd scenes. It is an important scenario, because most of the surveillance systems generate poor-quality videos, though current state-of-the-art methods may not be effectively applicable. Therefore recognizing actions performed by individuals in poor-quality spectator crowd scenes is an unsolved problem. In such cases, the main challenge is localizing person proposals for each actor in the crowd. This challenge becomes more difficult when occlusion is severe. In this work, we propose a novel approach to find person proposals in poor-quality spectator crowds using crowd-based constraints. First, we define persons in the crowd by using efficient person head detectors. We exploit person head size to estimate the person bounding box using linear regression. Then, we use distribution of heads in the crowd image to estimate more accurate person proposals. Motion trajectories are independently computed in the video without considering persons and then assigned to each person based on a novel distance measure computed between the trajectory and the person proposal. The set of trajectories and associated motion and texture-based features in overlapped time windows are used to compute the final feature vector. For each time window using early information fusion in the bag of visual-words framework, cumulative feature vectors are computed encoding action information. Experiments are performed on a publicly available real-world spectator crowd dataset containing as many as 150 actors performing multiple actions at the same time. Our experiments have demonstrated excellent performance of the proposed technique. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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Action recognition in poor-quality spectator crowd videos using head distribution-based person segmentation |
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https://dx.doi.org/10.1007/s00138-019-01039-3 |
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Al-Maadeed, Somaya |
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10.1007/s00138-019-01039-3 |
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| score |
7.3986635 |