Analyzing crowd behavior in naturalistic conditions: Identifying sources and sinks and characterizing main flows
Pedestrians, in videos taken from fixed cameras, tend to appear and disappear at precise locations such as doors, gateways or edges of the scene: we refer to locations where pedestrians appear as sources (potential origins) and the locations where they disappear as sinks (potential destinations). Th...
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| Vydané v: | Neurocomputing Ročník 177; s. 543 - 563 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
12.02.2016
Elsevier BV |
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | Pedestrians, in videos taken from fixed cameras, tend to appear and disappear at precise locations such as doors, gateways or edges of the scene: we refer to locations where pedestrians appear as sources (potential origins) and the locations where they disappear as sinks (potential destinations). The detection of these points and the characterization of the flows connecting them represent a typical preliminary step in most pedestrian studies and it can be supported by computer vision approaches. In this paper we propose an algorithm in which a scene is overlaid by a grid of particles initializing a dynamical system defined by optical flow, a high level global motion information. Time integration of the dynamical system produces short particle trajectories (tracklets), representing dense but short motion patterns in segments of the scene; tracklets are then extended into longer tracks that are grouped using an unsupervised clustering algorithm, where the similarity is measured by the Longest Common Subsequence. The analysis of these clusters supports the identification of sources and sinks related to a single video segment. Local segment information is finally combined to achieve a global set of traces identifying sources and sinks, and characterizing the flow of pedestrians connecting them. The paper presents the defined technique and it discusses its application in a real-world scenario. |
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| AbstractList | Pedestrians, in videos taken from fixed cameras, tend to appear and disappear at precise locations such as doors, gateways or edges of the scene: we refer to locations where pedestrians appear as sources (potential origins) and the locations where they disappear as sinks (potential destinations). The detection of these points and the characterization of the flows connecting them represent a typical preliminary step in most pedestrian studies and it can be supported by computer vision approaches. In this paper we propose an algorithm in which a scene is overlaid by a grid of particles initializing a dynamical system defined by optical flow, a high level global motion information. Time integration of the dynamical system produces short particle trajectories (tracklets), representing dense but short motion patterns in segments of the scene; tracklets are then extended into longer tracks that are grouped using an unsupervised clustering algorithm, where the similarity is measured by the Longest Common Subsequence. The analysis of these clusters supports the identification of sources and sinks related to a single video segment. Local segment information is finally combined to achieve a global set of traces identifying sources and sinks, and characterizing the flow of pedestrians connecting them. The paper presents the defined technique and it discusses its application in a real-world scenario. |
| Author | Basalamah, Saleh Khan, Sultan D. Bandini, Stefania Vizzari, Giuseppe |
| Author_xml | – sequence: 1 givenname: Sultan D. surname: Khan fullname: Khan, Sultan D. email: sultan.khan@disco.unimib.it organization: Complex Systems and Artificial Intelligence Research Centre, Università degli Studi di Milano–Bicocca, Milano, Italy – sequence: 2 givenname: Stefania surname: Bandini fullname: Bandini, Stefania email: bandini@disco.unimib.it organization: Complex Systems and Artificial Intelligence Research Centre, Università degli Studi di Milano–Bicocca, Milano, Italy – sequence: 3 givenname: Saleh surname: Basalamah fullname: Basalamah, Saleh email: smbasalamah@uqu.edu.sa organization: Department of Computer Engineering, Umm Al Qura University, Makkah, Saudi Arabia – sequence: 4 givenname: Giuseppe orcidid: 0000-0002-7916-6438 surname: Vizzari fullname: Vizzari, Giuseppe email: vizzari@disco.unimib.it organization: Complex Systems and Artificial Intelligence Research Centre, Università degli Studi di Milano–Bicocca, Milano, Italy |
| BackLink | https://cir.nii.ac.jp/crid/1871428068200411264$$DView record in CiNii |
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| Cites_doi | 10.1109/CVPRW.2003.10036 10.1109/JSTSP.2008.2001306 10.1007/978-3-540-24673-2_3 10.1109/TAES.2006.1603404 10.1007/978-3-642-15555-0_21 10.1109/CVPR.2004.1315192 10.1007/s11263-008-0136-6 10.1109/CVPR.2007.382977 10.1109/TPAMI.2012.123 10.18637/jss.v031.i10 10.1109/TPAMI.2013.103 10.1016/j.neucom.2012.01.036 10.1109/CC.2013.6506940 10.1109/AVSS.2010.41 10.1007/s11263-005-1838-7 10.1109/ICCV.2009.5459154 10.1109/ICDSC.2007.4357505 10.1023/A:1009745219419 10.1007/978-3-642-15549-9_42 10.1007/978-3-540-24673-2_23 10.1109/ICCVW.2009.5457659 10.1109/CVPR.1994.323794 10.1007/978-3-642-33709-3_25 10.1109/CVPR.2011.5995459 10.1109/AVSS.2010.79 10.1109/ICIAP.2007.4362878 10.1109/CVPR.2006.320 10.1109/ICPR.2006.392 10.1023/B:VISI.0000011205.11775.fd 10.1109/ICCV.2009.5459286 10.1109/ICPR.2010.862 10.1109/ICME.2010.5583046 10.1145/1247480.1247546 10.1007/978-3-642-17289-2_12 10.1109/ICCV.2009.5459301 10.1016/j.patrec.2013.10.003 10.1109/CVPRW.2009.5206721 |
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| References | Boltes, Seyfried (bib10) 2013; 100 Solmaz, Moore, Shah (bib3) 2012; 34 B. Zhou, X. Wang, X. Tang, Random field topic model for semantic region analysis in crowded scenes from tracklets, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011, pp. 3441–3448. A.M. Cheriyadat, R.J. Radke, Automatically determining dominant motions in crowded scenes by clustering partial feature trajectories, in: Proceedings of the First ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC׳07 2007, pp. 52–58. J. Shi, C. Tomasi, Good features to track, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR׳94 1994, pp. 593–600. Baker, Matthews (bib11) 2004; 56 J. Sun, Y. Mu, S. Yan, L.-F. Cheong, Activity recognition using dense long-duration trajectories, in: Proceedings of 2010 IEEE International Conference on Multimedia and Expo (ICME) 2010, pp. 322–327. T. Brox, J. Malik, Object segmentation by long term analysis of point trajectories, in: Computer Vision–ECCV 2010, Springer, 2010, pp. 282–295. Berens (bib40) 2009; 31 G. J. Brostow, R. Cipolla, Unsupervised bayesian detection of independent motion in crowds, in: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1 2006, pp. 594–601. S.D. Khan, G. Vizzari, S. Bandini, S. Basalamah, Detecting dominant motion flows and people counting in high density crowds. Bandini, Gorrini, Vizzari (bib38) 2014; 44 Laptev (bib16) 2005; 64 Cheriyadat, Radke (bib27) 2008; 4 Milan, Roth, Schindler (bib45) 2014; 36 M. Raptis, S. Soatto, Tracklet descriptors for action modeling and video analysis, in: Computer Vision–ECCV 2010, Springer, 2010, pp. 577–590. Sander, Ester, Kriegel, Xu (bib32) 1998; 2 S. Ali, M. Shah, A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis, in: CVPR, 2007. Chongjing, Xu, Yi, Yuncai (bib28) 2013; 10 B. Zhou, X. Wang, X. Tang, Understanding collective crowd behaviors: learning a mixture model of dynamic pedestrian-agents, in: Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 2871–2878. R. Messing, C. Pal, H. Kautz, Activity recognition using the velocity histories of tracked keypoints, in: Proceedings of IEEE 12th International Conference on Computer Vision, 2009 2009, pp. 104–111. Hue, Cadre, Perez (bib6) 2006; 42 Sand, Teller (bib14) 2008; 80 Junior, Musse, Jung (bib2) 2010; 27 R. Challenger, C.W. Clegg, M.A. Robinson, Understanding Crowd Behaviours: Supporting Evidence, Tech. Rep., University of Leeds, 2009. A.R. Zamir, A. Dehghan, M. Shah, Gmcp-tracker: Global multi-object tracking using generalized minimum clique graphs, in: Computer Vision–ECCV 2012, Springer, 2012, pp. 343–356. J. Sun, X. Wu, S. Yan, L.-F. Cheong, T.-S. Chua, J. Li, Hierarchical spatio-temporal context modeling for action recognition, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 2004–2011. D. Sugimura, K.M. Kitani, T. Okabe, Y. Sato, A. Sugimoto, Using individuality to track individuals: clustering individual trajectories in crowds using local appearance and frequency trait, in: Proceedings of 2009 12th IEEE International Conference on Computer Vision 2009, pp. 1467–1474. O. Ozturk, T. Yamasaki, K. Aizawa, Detecting dominant motion flows in unstructured/structured crowd scenes, in: Proceedings of the 20th International Conference on Pattern Recognition (ICPR), 2010, pp. 3533–3536. M. Rodriguez, S. Ali, T. Kanade, Tracking in unstructured crowded scenes, in: Proceedings of IEEE 12th International Conference on Computer Vision, ICCV 2009, Kyoto, Japan, September 27 - October 4, 2009, 2009, pp. 1389–1396. K. Sankaranarayanan, J.W. Davis, Learning directed intention-driven activities using co-clustering., in: AVSS, 2010, pp. 400–407. T. Zhao, R. Nevatia, Tracking multiple humans in crowded environment, in: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004. IEEE, 2004, pp. II–406. M.L. Federici, A. Gorrini, L. Manenti, G. Vizzari, Data collection for modeling and simulation: case study at the university of milan-bicocca, in: G. C. Sirakoulis, S. Bandini (Eds.), ACRI, vol. 7495 of Lecture Notes in Computer Science, Springer, 2012, pp. 699–708. S. Battiato, G. Gallo, G. Puglisi, S. Scellato, Sift features tracking for video stabilization, in: Proceedings of the 14th International Conference on Image Analysis and Processing, ICIAP 2007, pp. 825–830. Z. Zhang, K. Huang, T. Tan, Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes, in: Proceedings of the 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3 2006, pp. 1135–1138. C. Stauffer, Estimating tracking sources and sinks, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshop, CVPRW׳03, vol. 4, 2003, pp. 35–35. T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, in: Computer Vision-ECCV 2004, Springer, 2004, pp. 25–36. J.-G. Lee, J. Han, K.-Y. Whang, Trajectory clustering: a partition-and-group framework, in: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, ACM, 2007, pp. 593–604. Z. Khan, T. Balch, F. Dellaert, An mcmc-based particle filter for tracking multiple interacting targets, in: Computer Vision-ECCV 2004, Springer, 2004, pp. 279–290. M. Nedrich, J.W. Davis, Learning scene entries and exits using coherent motion regions, in: Advances in Visual Computing, Springer, 2010, pp. 120–131. P. Matikainen, M. Hebert, R. Sukthankar, Trajectons: action recognition through the motion analysis of tracked features, in: Proceedings of 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), 2009, pp. 514–521. B.D. Lucas, T. Kanade, et al., An iterative image registration technique with an application to stereo vision., in: IJCAI, 81, 1981, pp. 674–679. M. Vlachos, G. Kollios, D. Gunopulos, Discovering similar multidimensional trajectories, in: Proceedings of the 18th International Conference on Data Engineering, 2002 2002, pp. 673–684. W.-C. Lu, Y.-C. Wang, C.-S. Chen, Learning dense optical-flow trajectory patterns for video object extraction, in: Proceedings of 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) 2010, pp. 315–322. P. Berens, M.J. Velasco, The Circular Statistics Toolbox for Matlab, MPI Technical Report No. 184. Bandini (10.1016/j.neucom.2015.11.049_bib38) 2014; 44 10.1016/j.neucom.2015.11.049_bib7 10.1016/j.neucom.2015.11.049_bib5 Chongjing (10.1016/j.neucom.2015.11.049_bib28) 2013; 10 10.1016/j.neucom.2015.11.049_bib4 Milan (10.1016/j.neucom.2015.11.049_bib45) 2014; 36 10.1016/j.neucom.2015.11.049_bib9 10.1016/j.neucom.2015.11.049_bib8 Junior (10.1016/j.neucom.2015.11.049_bib2) 2010; 27 10.1016/j.neucom.2015.11.049_bib19 10.1016/j.neucom.2015.11.049_bib15 10.1016/j.neucom.2015.11.049_bib37 10.1016/j.neucom.2015.11.049_bib1 10.1016/j.neucom.2015.11.049_bib18 Boltes (10.1016/j.neucom.2015.11.049_bib10) 2013; 100 Sand (10.1016/j.neucom.2015.11.049_bib14) 2008; 80 10.1016/j.neucom.2015.11.049_bib17 10.1016/j.neucom.2015.11.049_bib39 10.1016/j.neucom.2015.11.049_bib12 10.1016/j.neucom.2015.11.049_bib34 Hue (10.1016/j.neucom.2015.11.049_bib6) 2006; 42 Sander (10.1016/j.neucom.2015.11.049_bib32) 1998; 2 10.1016/j.neucom.2015.11.049_bib33 Laptev (10.1016/j.neucom.2015.11.049_bib16) 2005; 64 10.1016/j.neucom.2015.11.049_bib36 10.1016/j.neucom.2015.11.049_bib13 10.1016/j.neucom.2015.11.049_bib35 10.1016/j.neucom.2015.11.049_bib30 10.1016/j.neucom.2015.11.049_bib31 Baker (10.1016/j.neucom.2015.11.049_bib11) 2004; 56 Cheriyadat (10.1016/j.neucom.2015.11.049_bib27) 2008; 4 10.1016/j.neucom.2015.11.049_bib26 Solmaz (10.1016/j.neucom.2015.11.049_bib3) 2012; 34 10.1016/j.neucom.2015.11.049_bib29 10.1016/j.neucom.2015.11.049_bib23 10.1016/j.neucom.2015.11.049_bib22 10.1016/j.neucom.2015.11.049_bib44 10.1016/j.neucom.2015.11.049_bib25 10.1016/j.neucom.2015.11.049_bib24 Berens (10.1016/j.neucom.2015.11.049_bib40) 2009; 31 10.1016/j.neucom.2015.11.049_bib41 10.1016/j.neucom.2015.11.049_bib21 10.1016/j.neucom.2015.11.049_bib43 10.1016/j.neucom.2015.11.049_bib20 10.1016/j.neucom.2015.11.049_bib42 |
| References_xml | – reference: B. Zhou, X. Wang, X. Tang, Random field topic model for semantic region analysis in crowded scenes from tracklets, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011, pp. 3441–3448. – reference: M. Raptis, S. Soatto, Tracklet descriptors for action modeling and video analysis, in: Computer Vision–ECCV 2010, Springer, 2010, pp. 577–590. – reference: T. Brox, J. Malik, Object segmentation by long term analysis of point trajectories, in: Computer Vision–ECCV 2010, Springer, 2010, pp. 282–295. – reference: K. Sankaranarayanan, J.W. Davis, Learning directed intention-driven activities using co-clustering., in: AVSS, 2010, pp. 400–407. – volume: 27 start-page: 66 year: 2010 end-page: 77 ident: bib2 article-title: Crowd analysis using computer vision techniques publication-title: IEEE Signal Process. Mag. – reference: M. Nedrich, J.W. Davis, Learning scene entries and exits using coherent motion regions, in: Advances in Visual Computing, Springer, 2010, pp. 120–131. – reference: J.-G. Lee, J. Han, K.-Y. Whang, Trajectory clustering: a partition-and-group framework, in: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, ACM, 2007, pp. 593–604. – reference: A.M. Cheriyadat, R.J. Radke, Automatically determining dominant motions in crowded scenes by clustering partial feature trajectories, in: Proceedings of the First ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC׳07 2007, pp. 52–58. – reference: S.D. Khan, G. Vizzari, S. Bandini, S. Basalamah, Detecting dominant motion flows and people counting in high density crowds. – reference: B.D. Lucas, T. Kanade, et al., An iterative image registration technique with an application to stereo vision., in: IJCAI, 81, 1981, pp. 674–679. – reference: M. Vlachos, G. Kollios, D. Gunopulos, Discovering similar multidimensional trajectories, in: Proceedings of the 18th International Conference on Data Engineering, 2002 2002, pp. 673–684. – reference: J. Sun, Y. Mu, S. Yan, L.-F. Cheong, Activity recognition using dense long-duration trajectories, in: Proceedings of 2010 IEEE International Conference on Multimedia and Expo (ICME) 2010, pp. 322–327. – volume: 80 start-page: 72 year: 2008 end-page: 91 ident: bib14 article-title: Particle video publication-title: Int. J. Comput. Vis. – reference: P. Berens, M.J. Velasco, The Circular Statistics Toolbox for Matlab, MPI Technical Report No. 184. – volume: 36 start-page: 58 year: 2014 end-page: 72 ident: bib45 article-title: Continuous energy minimization for multitarget tracking publication-title: IEEE TPAMI – reference: J. Sun, X. Wu, S. Yan, L.-F. Cheong, T.-S. Chua, J. Li, Hierarchical spatio-temporal context modeling for action recognition, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 2004–2011. – volume: 10 start-page: 144 year: 2013 end-page: 154 ident: bib28 article-title: Analyzing motion patterns in crowded scenes via automatic tracklets clustering publication-title: Communications, China – volume: 34 start-page: 2064 year: 2012 end-page: 2070 ident: bib3 article-title: Identifying behaviors in crowd scenes using stability analysis for dynamical systems publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: C. Stauffer, Estimating tracking sources and sinks, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshop, CVPRW׳03, vol. 4, 2003, pp. 35–35. – volume: 31 start-page: 1 year: 2009 end-page: 21 ident: bib40 article-title: Circstat publication-title: J. Stat. Softw. – reference: T. Zhao, R. Nevatia, Tracking multiple humans in crowded environment, in: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004. IEEE, 2004, pp. II–406. – reference: B. Zhou, X. Wang, X. Tang, Understanding collective crowd behaviors: learning a mixture model of dynamic pedestrian-agents, in: Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 2871–2878. – reference: P. Matikainen, M. Hebert, R. Sukthankar, Trajectons: action recognition through the motion analysis of tracked features, in: Proceedings of 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), 2009, pp. 514–521. – volume: 4 start-page: 568 year: 2008 end-page: 581 ident: bib27 article-title: Detecting dominant motions in dense crowds publication-title: IEEE J. Sel. Top. Signal Process. – volume: 100 start-page: 127 year: 2013 end-page: 133 ident: bib10 article-title: Collecting pedestrian trajectories publication-title: Neurocomputing – reference: T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, in: Computer Vision-ECCV 2004, Springer, 2004, pp. 25–36. – reference: M.L. Federici, A. Gorrini, L. Manenti, G. Vizzari, Data collection for modeling and simulation: case study at the university of milan-bicocca, in: G. C. Sirakoulis, S. Bandini (Eds.), ACRI, vol. 7495 of Lecture Notes in Computer Science, Springer, 2012, pp. 699–708. – volume: 44 start-page: 16 year: 2014 end-page: 29 ident: bib38 article-title: Towards an integrated approach to crowd analysis and crowd synthesis publication-title: Pattern Recognit. Lett. – reference: W.-C. Lu, Y.-C. Wang, C.-S. Chen, Learning dense optical-flow trajectory patterns for video object extraction, in: Proceedings of 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) 2010, pp. 315–322. – volume: 64 start-page: 107 year: 2005 end-page: 123 ident: bib16 article-title: On space-time interest points publication-title: Int. J. Comput. Vis. – volume: 56 start-page: 221 year: 2004 end-page: 255 ident: bib11 article-title: Lucas-kanade 20 years on publication-title: Int. J. Comput. Vis. – reference: M. Rodriguez, S. Ali, T. Kanade, Tracking in unstructured crowded scenes, in: Proceedings of IEEE 12th International Conference on Computer Vision, ICCV 2009, Kyoto, Japan, September 27 - October 4, 2009, 2009, pp. 1389–1396. – reference: D. Sugimura, K.M. Kitani, T. Okabe, Y. Sato, A. Sugimoto, Using individuality to track individuals: clustering individual trajectories in crowds using local appearance and frequency trait, in: Proceedings of 2009 12th IEEE International Conference on Computer Vision 2009, pp. 1467–1474. – volume: 42 start-page: 37 year: 2006 end-page: 49 ident: bib6 article-title: Posterior cramer-rao bounds for multi-target tracking publication-title: IEEE Trans. Aerosp. Electron. Syst. – reference: S. Ali, M. Shah, A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis, in: CVPR, 2007. – reference: J. Shi, C. Tomasi, Good features to track, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR׳94 1994, pp. 593–600. – reference: G. J. Brostow, R. Cipolla, Unsupervised bayesian detection of independent motion in crowds, in: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1 2006, pp. 594–601. – volume: 2 start-page: 169 year: 1998 end-page: 194 ident: bib32 article-title: Density-based clustering in spatial databases publication-title: Data Min. Knowl. Discov. – reference: Z. Khan, T. Balch, F. Dellaert, An mcmc-based particle filter for tracking multiple interacting targets, in: Computer Vision-ECCV 2004, Springer, 2004, pp. 279–290. – reference: R. Challenger, C.W. Clegg, M.A. Robinson, Understanding Crowd Behaviours: Supporting Evidence, Tech. Rep., University of Leeds, 2009. – reference: Z. Zhang, K. Huang, T. Tan, Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes, in: Proceedings of the 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3 2006, pp. 1135–1138. – reference: O. Ozturk, T. Yamasaki, K. Aizawa, Detecting dominant motion flows in unstructured/structured crowd scenes, in: Proceedings of the 20th International Conference on Pattern Recognition (ICPR), 2010, pp. 3533–3536. – reference: R. Messing, C. Pal, H. Kautz, Activity recognition using the velocity histories of tracked keypoints, in: Proceedings of IEEE 12th International Conference on Computer Vision, 2009 2009, pp. 104–111. – reference: S. Battiato, G. Gallo, G. Puglisi, S. Scellato, Sift features tracking for video stabilization, in: Proceedings of the 14th International Conference on Image Analysis and Processing, ICIAP 2007, pp. 825–830. – reference: A.R. Zamir, A. Dehghan, M. Shah, Gmcp-tracker: Global multi-object tracking using generalized minimum clique graphs, in: Computer Vision–ECCV 2012, Springer, 2012, pp. 343–356. – ident: 10.1016/j.neucom.2015.11.049_bib12 doi: 10.1109/CVPRW.2003.10036 – volume: 4 start-page: 568 issue: 2 year: 2008 ident: 10.1016/j.neucom.2015.11.049_bib27 article-title: Detecting dominant motions in dense crowds publication-title: IEEE J. Sel. Top. Signal Process. doi: 10.1109/JSTSP.2008.2001306 – ident: 10.1016/j.neucom.2015.11.049_bib29 doi: 10.1007/978-3-540-24673-2_3 – volume: 42 start-page: 37 issue: 1 year: 2006 ident: 10.1016/j.neucom.2015.11.049_bib6 article-title: Posterior cramer-rao bounds for multi-target tracking publication-title: IEEE Trans. Aerosp. Electron. Syst. doi: 10.1109/TAES.2006.1603404 – ident: 10.1016/j.neucom.2015.11.049_bib23 doi: 10.1007/978-3-642-15555-0_21 – ident: 10.1016/j.neucom.2015.11.049_bib22 – ident: 10.1016/j.neucom.2015.11.049_bib4 doi: 10.1109/CVPR.2004.1315192 – volume: 80 start-page: 72 issue: 1 year: 2008 ident: 10.1016/j.neucom.2015.11.049_bib14 article-title: Particle video publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-008-0136-6 – ident: 10.1016/j.neucom.2015.11.049_bib36 doi: 10.1109/CVPR.2007.382977 – volume: 34 start-page: 2064 issue: 10 year: 2012 ident: 10.1016/j.neucom.2015.11.049_bib3 article-title: Identifying behaviors in crowd scenes using stability analysis for dynamical systems publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2012.123 – volume: 31 start-page: 1 issue: 10 year: 2009 ident: 10.1016/j.neucom.2015.11.049_bib40 article-title: Circstat publication-title: J. Stat. Softw. doi: 10.18637/jss.v031.i10 – volume: 36 start-page: 58 issue: 1 year: 2014 ident: 10.1016/j.neucom.2015.11.049_bib45 article-title: Continuous energy minimization for multitarget tracking publication-title: IEEE TPAMI doi: 10.1109/TPAMI.2013.103 – volume: 100 start-page: 127 issue: 0 year: 2013 ident: 10.1016/j.neucom.2015.11.049_bib10 article-title: Collecting pedestrian trajectories publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.01.036 – ident: 10.1016/j.neucom.2015.11.049_bib26 – ident: 10.1016/j.neucom.2015.11.049_bib34 – volume: 10 start-page: 144 issue: 4 year: 2013 ident: 10.1016/j.neucom.2015.11.049_bib28 article-title: Analyzing motion patterns in crowded scenes via automatic tracklets clustering publication-title: Communications, China doi: 10.1109/CC.2013.6506940 – ident: 10.1016/j.neucom.2015.11.049_bib44 doi: 10.1109/AVSS.2010.41 – volume: 64 start-page: 107 issue: 2–3 year: 2005 ident: 10.1016/j.neucom.2015.11.049_bib16 article-title: On space-time interest points publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-005-1838-7 – ident: 10.1016/j.neucom.2015.11.049_bib30 – ident: 10.1016/j.neucom.2015.11.049_bib15 doi: 10.1109/ICCV.2009.5459154 – ident: 10.1016/j.neucom.2015.11.049_bib35 doi: 10.1109/ICDSC.2007.4357505 – volume: 2 start-page: 169 issue: 2 year: 1998 ident: 10.1016/j.neucom.2015.11.049_bib32 article-title: Density-based clustering in spatial databases publication-title: Data Min. Knowl. Discov. doi: 10.1023/A:1009745219419 – ident: 10.1016/j.neucom.2015.11.049_bib20 doi: 10.1007/978-3-642-15549-9_42 – ident: 10.1016/j.neucom.2015.11.049_bib5 doi: 10.1007/978-3-540-24673-2_23 – ident: 10.1016/j.neucom.2015.11.049_bib17 doi: 10.1109/ICCVW.2009.5457659 – ident: 10.1016/j.neucom.2015.11.049_bib41 doi: 10.1109/CVPR.1994.323794 – ident: 10.1016/j.neucom.2015.11.049_bib42 – ident: 10.1016/j.neucom.2015.11.049_bib9 doi: 10.1007/978-3-642-33709-3_25 – ident: 10.1016/j.neucom.2015.11.049_bib21 doi: 10.1109/CVPR.2011.5995459 – volume: 27 start-page: 66 issue: 5 year: 2010 ident: 10.1016/j.neucom.2015.11.049_bib2 article-title: Crowd analysis using computer vision techniques publication-title: IEEE Signal Process. Mag. – ident: 10.1016/j.neucom.2015.11.049_bib24 doi: 10.1109/AVSS.2010.79 – ident: 10.1016/j.neucom.2015.11.049_bib43 doi: 10.1109/ICIAP.2007.4362878 – ident: 10.1016/j.neucom.2015.11.049_bib7 doi: 10.1109/CVPR.2006.320 – ident: 10.1016/j.neucom.2015.11.049_bib33 doi: 10.1109/ICPR.2006.392 – volume: 56 start-page: 221 issue: 3 year: 2004 ident: 10.1016/j.neucom.2015.11.049_bib11 article-title: Lucas-kanade 20 years on publication-title: Int. J. Comput. Vis. doi: 10.1023/B:VISI.0000011205.11775.fd – ident: 10.1016/j.neucom.2015.11.049_bib8 doi: 10.1109/ICCV.2009.5459286 – ident: 10.1016/j.neucom.2015.11.049_bib39 doi: 10.1109/ICPR.2010.862 – ident: 10.1016/j.neucom.2015.11.049_bib19 doi: 10.1109/ICME.2010.5583046 – ident: 10.1016/j.neucom.2015.11.049_bib31 doi: 10.1145/1247480.1247546 – ident: 10.1016/j.neucom.2015.11.049_bib37 – ident: 10.1016/j.neucom.2015.11.049_bib13 doi: 10.1007/978-3-642-17289-2_12 – ident: 10.1016/j.neucom.2015.11.049_bib25 doi: 10.1109/ICCV.2009.5459301 – volume: 44 start-page: 16 year: 2014 ident: 10.1016/j.neucom.2015.11.049_bib38 article-title: Towards an integrated approach to crowd analysis and crowd synthesis publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2013.10.003 – ident: 10.1016/j.neucom.2015.11.049_bib1 – ident: 10.1016/j.neucom.2015.11.049_bib18 doi: 10.1109/CVPRW.2009.5206721 |
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| Title | Analyzing crowd behavior in naturalistic conditions: Identifying sources and sinks and characterizing main flows |
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