References
- Rigoll G, Eickeler S, Muller S. Person tracking in real-world scenarios using statistical methods. Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580); 2000; Grenoble, France. p. 342–347. doi:10.1109/AFGR.2000.840657.
- Yamamoto M, Ohta T, Yamagiwa T, et al. Human action tracking guided by key-frames. Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580); 2000; Grenoble, France. p. 354–361. doi:10.1109/AFGR.2000.840659.
- Duan S, Wang X, Yu X. Crowded abnormal detection based on mixture of kernel dynamic texture. 2014 International Conference on Audio, Language and Image Processing; 2014; Shanghai. p. 931–936. doi:10.1109/ICALIP.2014.7009931.
- Xu J, Denman S, Sridharan S, et al. Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes. Proceedings of the 2011 joint ACM workshop on Modeling and representing events (J-MRE '11). New York (NY): Association for Computing Machinery; 2011. p. 25–30. doi:10.1145/2072508.2072515.
- Kratz L, Nishino K. Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. 2009 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work. CVPR Work. 2009;1446–1453. doi:10.1109/CVPRW.2009.5206771.
- Kratz L, Member S, Nishino K. Spatio-temporal motion patterns in extremely crowded scenes. Analysis. 2012;34(5):987–1002.
- Roshtkhari MJ, Levine MD. An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions. Comput. Vis. Image Underst. 2013;117(10):1436–1452.
- Su H, Yang H, Zheng S, et al. The large-scale crowd behavior perception based on spatio-temporal viscous fluid field. IEEE Trans. Inf. Forensics Secur. 2013;8(10):1575–1589. doi:10.1109/TIFS.2013.2277773.
- Wang J, Xu Z. Spatio-temporal texture modelling for real-time crowd anomaly detection. Comput. Vis. Image Underst. 2016;144:177–187.
- Du D, Qi H, Huang Q, et al. Abnormal event detection in crowded scenes based on structural multi-scale motion interrelated patterns. Proc. - IEEE Int. Conf. Multimed. Expo. 2013, doi:10.1109/ICME.2013.6607499.
- Zhou S, Shen W, Zeng D, et al. Spatial – temporal convolutional neural networks for anomaly detection and localization in crowded scenes. Signal Process. Image Commun. 2016;47:358–368. doi:10.1016/j.image.2016.06.007.
- Cheriyadat AM, Radke RJ. Automatically determining dominant motions in crowded scenes by clustering partial feature trajectories. 2007 First ACM/IEEE International Conference on Distributed Smart Cameras; 2007; Vienna. p. 52–58. doi:10.1109/ICDSC.2007.4357505.
- Haritaoglu I, Harwood D, Davis LS. W4: real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Mach. Intell. 2000;22(8):809–830.
- Cheriyadat AM, Radke RJ. Detecting dominant motions in dense crowds. IEEE J. Sel. Top. Signal Process. 2008;2(4):568–581. doi:10.1109/JSTSP.2008.2001306.
- Wang C, Zhao X, Zou Y, et al. Analyzing motion patterns in crowded scenes via automatic tracklets clustering. China Commun. 2013;10(4):144–154. doi:10.1109/CC.2013.6506940.
- Bera A, Kim S, Manocha D. Realtime anomaly detection using trajectory-level crowd behavior learning. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work. 2016;1289–1296. doi:10.1109/CVPRW.2016.163.
- Zou Y, Zhao X, Liu Y. Detect coherent motions in crowd scenes based on tracklets association. Proc Int Conf Image Process, ICIP. 2015;2015-Decem:4456–4460. doi:10.1109/ICIP.2015.7351649.
- Feng L, Bhanu B. Understanding dynamic social grouping behaviors of pedestrians. IEEE J Sel Top Signal Process. 2015;9(2):317–329. doi:10.1109/JSTSP.2014.2365765.
- Dalal N, Triggs B. Histograms of oriented gradients for human detection. EEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). 2005;886–893.
- Wang T, Delaunay-lms IC. Histograms of optical flow orientation for visual abnormal events detection. IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance. 2012;13–18. doi:10.1109/AVSS.2012.39.
- Cong Y, Yuan J, Liu J. Abnormal event detection in crowded scenes using sparse representation. Pattern Recognit. 2013;46(7):1851–1864. doi:10.1016/j.patcog.2012.11.021.
- Wang Q, Ma Q, Luo C-H, et al. Hybrid histogram of oriented optical flow for abnormal behavior detection in crowd scenes. Int. J. Pattern Recognit. Artif. Intell. 2016;30(2):1–14.
- Yu Y, Shen W, Huang H, et al. Abnormal event detection in crowded scenes using two sparse dictionaries with saliency. J Electron Imaging. 2017;26(3):033013, doi:10.1117/1.JEI.26.3.033013.
- McKenna SJ, Jabri S, Duric Z, et al. Tracking groups of people. Comput Vis Image Underst. 2000;80(1):42–56.
- Kaltsa V, et al. Swarm intelligence for detecting interesting events in crowded environments. IEEE Trans. Image Process. 2015;24(7):2153–2166.
- Jodoin PM, Benezeth Y, Wang Y. Meta-tracking for video scene understanding. 2013 10th IEEE Int Conf Adv Video Signal Based Surveillance, AVSS 2013. 2013;1–6. doi:10.1109/AVSS.2013.6636607.
- Hu M, Ali S, Shah M. Detecting global motion patterns in complex videos. 2008 19th International Conference on Pattern Recognition; 2008; Tampa, FL. p. 1–5. doi:10.1109/ICPR.2008.4760950.
- Wu S, Wong HS, Yu Z. A Bayesian model for crowd escape behavior detection. IEEE Trans. Circuits Syst. Video Technol. 2014;24(1):85–98. doi:10.1109/TCSVT.2013.2276151.
- Sasikala S, Udhaya R. Crowd escape behavior speed and location detection using bayesian model; 2015. pp. 42–48.
- Rao AS, Gubbi J, Marusic S, et al. Probabilistic detection of crowd events on riemannian manifolds. 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014. 2015.
- Rao AS, Member S, Gubbi J, et al. Crowd event detection on optical flow manifolds. IEEE Trans. Cybern. 2016;46(7):1524–1537.
- https://en.wikipedia.org/wiki/Gestalt_psychology.
- Hu MHM, Ali S, Shah M. Learning motion patterns in crowded scenes using motion flow field. 2008 19th Int. Conf. Pattern Recognit. 2008;2–6. doi:10.1109/ICPR.2008.4761183.
- Chen DY, Huang PC. Motion-based unusual event detection in human crowds. J. Vis. Commun. Image Represent. 2011;22(2):178–186. doi:10.1016/j.jvcir.2010.12.004.
- Stauffer C, Grimson WEL. Adaptive background mixture models for real-time tracking. Proc. 1999 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Cat No PR00149. 1999;2(c):246–252. doi:10.1109/CVPR.1999.784637.
- Hu W, Xiao X, Fu Z, et al. A system for learning statistical motion patterns. IEEE Trans. Pattern Anal. Mach. Intell. 2006;28(9):1450–1464. doi:10.1109/TPAMI.2006.176.
- Lawal IA, Poiesi F, Anguita D, et al. Support vector motion clustering. IEEE Trans. Circuits Syst. Video Technol. 2016;X(X):1–1. doi:10.1109/TCSVT.2016.2580401.
- Benabbas Y, Ihaddadene N, Djeraba C. Motion pattern extraction and event detection for automatic visual surveillance. EURASIP Journal on Image and Video Processing; 2011. doi:10.1155/2011/163682.
- Cong Y, Yuan J, Liu J. Sparse reconstruction cost for abnormal event detection. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2011;3449–3456. doi:10.1109/CVPR.2011.5995434.
- Cong Y, Yuan J, Tang Y. Video anomaly search in crowded scenes via spatio-temporal motion context. IEEE Trans. Inf. Forensics Secur. 2013;8(10):1590–1599. doi:10.1109/TIFS.2013.2272243.
- Thida M, Eng HL, Remagnino P. Laplacian eigenmap with temporal constraints for local abnormality detection in crowded scenes. IEEE Trans. Cybern. 2013;43(6):2147–2156. doi:10.1109/TCYB.2013.2242059.
- Wu Y, Ye Y, Zhao C. Coherent motion detection with collective density clustering. Proc 23rd ACM Int Conf Multimedia - MM ‘15. 2015;1(1):361–370. doi:10.1145/2733373.2806227.
- Mehran R, Oyama A, Shah M. Abnormal crowd behavior detection using social force model. 2009 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work. CVPR Work. 2009;2009(2):935–942. doi:10.1109/CVPRW.2009.5206641.
- Raghavendra R, Del Bue A, Cristani M, et al. Abnormal crowd behavior detection by social force optimization. Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 2011;7065 LNCS:134–145. doi:10.1007/978-3-642-25446-8_15.
- Zhang Y, Qin L, Ji R, et al. Social attribute-aware force model: exploiting richness of interaction for abnormal crowd detection. IEEE Trans. Circuits Syst. Video Technol. 2015;25(7):1231–1245. doi:10.1109/TCSVT.2014.2355711.
- Chetverikov D, Péteri R. A brief survey of dynamic texture description and recognition. In: Kurzyński M, Puchała E, Woźniak M, et al., editors. Computer recognition systems. Advances in soft computing. Vol 30. Berlin: Springer.
- Alahi A, Ramanathan V, Goel K, et al. Chapter 9 - Learning to predict human behavior in crowded scenes. In: Murino V, Cristani M, Shah S, et al., editors. Group and crowd behavior for computer vision. Academic Press; 2017. p. 183–207.
- Nelson RC, Polana R. Qualitative recognition of motion using temporal texture. CVGIP, Image Underst. 1992;56(1):78–89.
- Chan AB, Vasconcelos N. Modeling, clustering, and segmenting video with mixtures of dynamic textures. IEEE Trans. Pattern Anal. Mach. Intell. 2008;30(5):909–926. doi:10.1109/TPAMI.2007.70738.
- Mahadevan V, Li W, Bhalodia V, et al. Anomaly detection in crowded scenes. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2010; San Francisco, CA. p. 1975–1981. doi:10.1109/CVPR.2010.5539872.
- Li W, Mahadevan V, Vasconcelos N. Anomaly detection and localization in crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 2014;36(1):18–32. doi:10.1109/TPAMI.2013.111.
- Mehran R, Moore BE, Shah M. A streakline representation of flow in crowded scenes. Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 2010;6313 LNCS(PART 3):439–452. doi:10.1007/978-3-642-15558-1_32.
- Huang S, Huang D, Khuhro MA. Crowd motion analysis based on social force graph with streak flow attribute. J. Electr. Comput. Eng. 2015;2015; doi:10.1155/2015/492051.
- Solmaz B, Moore BE, Shah M. Identifying behaviors in crowd scenes using stability analysis for dynamical systems. IEEE Trans. Pattern Anal. Mach. Intell. 2012;34:2064–2070. doi:10.1109/TPAMI.2012.123.
- Zhang Y, Qin L, Yao H, et al. Beyond particle flow: bag of trajectory graphs for dense crowd event recognition. 2013 IEEE International Conference on Image Processing; 2013; Melbourne, VIC. p. 3572–3576. doi:10.1109/ICIP.2013.6738737.
- Zhang Y, Huang Q, Qin L, et al. Representing dense crowd patterns using bag of trajectory graphs. Signal, Image Video Process. 2014;8:173–181. doi:10.1007/s11760-014-0669-9.
- Nam Y, Hong S. Real-time abnormal situation detection based on particle advection in crowded scenes. J. Real-Time Image Process. 2015;10(4):771–784. doi:10.1007/s11554-014-0424-z.
- Lim MK, Kok VJ, Loy CC, et al. Crowd Saliency detection via global similarity structure. 2014 22nd International Conference on Pattern Recognition; 2014; Stockholm. p. 3957–3962. doi:10.1109/ICPR.2014.678.
- Wu S, Yang H, Zheng S, et al. Crowd behavior analysis via curl and divergence of motion trajectories. Int. J. Comput. Vis. 2017;1–21. doi:10.1007/s11263-017-1005-y.
- Wu S, Su H, Yang H, et al. Bilinear dynamics for crowd video analysis. J. Vis. Commun. Image Represent. 2017 [cited Jan]. doi:10.1016/j.jvcir.2017.01.026.
- Wu S, Yang H, Zheng S, et al. Motion sketch based crowd video retrieval. Multimed. Tools Appl. 2017;76(19):20167–20195. doi:10.1007/s11042-017-4568-2.
- Zhang Y, Qin L, Ji R, et al. Exploring coherent motion patterns via structured trajectory learning for crowd mood modeling. IEEE Trans. Circuits Syst. Video Technol. 2017;27(3):635–648. doi:10.1109/TCSVT.2016.2593609.
- Pereira EM, Cardoso JS, Morla R. Long-range trajectories from global and local motion representations. J. Vis. Commun. Image Represent. 2016;40:265–287. doi:10.1016/j.jvcir.2016.06.020.
- Wang X, He X, Wu X, et al. A classification method based on streak flow for abnormal crowd behaviors. Opt. - Int. J. Light Electron Opt. 2016;127(4):2386–2392. doi:10.1016/j.ijleo.2015.08.081.
- Wang X, Gao M, He X, et al. An abnormal crowd behavior detection algorithm based on fluid Mechanics. J. Comput. 2014;9(5):1144–1149. doi:10.4304/jcp.9.5.1144-1149.
- Wang X, Yang X, He X, et al. A high accuracy flow segmentation method in crowded scenes based on streakline. Opt. - Int. J. Light Electron Opt. 2014;125(3):924–929. doi:10.1016/j.ijleo.2013.07.166.
- Brox T, Papenberg N, Weickert J. High accuracy optical flow estimation based on a Theory for Warping. Comput. Vis. - ECCV. 2004;4(May):25–36. doi:10.1007/978-3-540-24673-2_3.
- Stephens K, Bros AG. Grouping multi-vector streaklines for human activity identification. 2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP); 2016; Bordeaux. p. 1–5. doi:10.1109/IVMSPW.2016.7528185.
- Ali S, Shah M. A Lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. 2007 IEEE Conference on Computer Vision and Pattern Recognition; 2007; Minneapolis, MN. p. 1–6. doi:10.1109/CVPR.2007.382977.
- Wu S, Moore BE, Shah M. Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2010; San Francisco, CA. p. 2054–2060. doi:10.1109/CVPR.2010.5539882.
- Polus BA, Ushpiz A, Division UT. Pedestrian flow and level of service. Transp. Res. Rec. 1981;109:46–56.
- http://www.gkstill.com/Support/crowd-flow/fruin/Fruin1.html.
- Bloomberg MR, Burden AM. New York city pedestrian level of service study phase I; 2006.
- Piccardi M. Background subtraction techniques: a review. 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), Vol. 4; 2004; The Hague. p. 3099–3104. doi:10.1109/ICSMC.2004.1400815
- Barnich O, Van Droogenbroeck M. Vibe: A universal background subtraction algorithm for video sequences. IEEE Trans Image Process. 2011;20(6):1709–1724. doi:10.1109/TIP.2010.2101613.
- Helbing D, Molnár P. Social force model for pedestrian dynamics. 1995;51:5, doi:10.1103/PhysRevE.51.4282.
- Helbing D, Molnar P. Self-Organization phenomena in pedestrian crowds. Condens. Matter. 1998: 569–577. doi:10.1068/b2697.
- Helbing D. A fluid dynamic model for the movement of pedestrians. Complex Syst. 1992;6:391–415. doi:citeulike-article-id:3945298.
- Shadden SC, Lekien F, Marsden JE. Definition and properties of Lagrangian coherent structures from finite-time Lyapunov exponents in two-dimensional aperiodic flows. Phys. D Nonlinear Phenom. 2005;212(3–4):271–304. doi:10.1016/j.physd.2005.10.007.
- Behera S, Member S, Dogra DP, et al. Understanding crowd flow movements using active-langevin model; 2020. pp. 1–10.
- Ravanbakhsh M, Nabi M, Mousavi H. Plug-and-play CNN for crowd motion analysis: an application in abnormal event detection; 2016.
- Ganokratanaa T, Member GS. Unsupervised anomaly detection and localization based on deep spatiotemporal translation network. IEEE Access. 2020;8:50312–50329. doi:10.1109/ACCESS.2020.2979869.
- Shao J, Loy CC, Kang K, et al. Crowded scene understanding by Deeply learned volumetric slices. IEEE Trans. Circuits Syst. Video Technol. 2017;27(3):613–623.
- Tripathi G, Singh K, Kumar D. Convolutional neural networks for crowd behaviour analysis : a survey. Vis. Comput. 2018, doi:10.1007/s00371-018-1499-5.
- “PETS2009 Dataset. [Online]. Available: http://www.cvg.rdg.ac.uk/ PETS2009.”.