International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 11, No. 10, 8 Oct. 2019
Cover page and Table of Contents: PDF (size: 1428KB)
Crowd Escape Event Detection via Pooling Features of Optical Flow for Intelligent Video Surveillance Systems
Full Text (PDF, 1428KB), PP.40-49
Views: 0 Downloads: 0
Author(s)
Gajendra Singh
1,*
Arun Khosla
2
Rajiv Kapoor
2
Index Terms
Crowd escape, Probabilistic weighted feature Probabilistic Weighted Optical Flow Magnitude Probabilistic Weighted Optical Flow Direction, One-Class ELM
Abstract
In this paper we propose a method for automatic detection of crowd escape behaviour. Motion features are extracted by optical flow using Lucas-Kanade derivative of Gaussian method (LKDoG) followed by robust probabilistic weighted feature pooling operation. Probabilistic feature polling chooses the most descriptive features in the sub-block and summarizes the joint representation of the selected features by Probabilistic Weighted Optical Flow Magnitude Histogram (PWOFMH) and Probabilistic Weighted Optical Flow Direction Histogram (PWOFDH). One class Extreme Learning Machine (OC-ELM) is used to train and test our proposed algorithm. The accuracy of our proposed method is evaluated on UMN, PETS 2009 and AVANUE datasets and correlations with the best in class techniques approves the upsides of our proposed method.
Cite This Paper
Gajendra Singh, Arun Khosla, Rajiv Kapoor, "Crowd Escape Event Detection via Pooling Features of Optical Flow for Intelligent Video Surveillance Systems", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.10, pp. 40-49, 2019. DOI: 10.5815/ijigsp.2019.10.06
Reference
[1]M. Heikkila, M. Pietikainen, “A texture-based method for modeling the background and detecting moving objects” IEEE transactions on pattern analysis and machine intelligence 28 (4) (2006) 657–662.
[2]L. Kratz, K. Nishino,” Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models”, Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, IEEE, 2009, pp. 1446–1453.
[3]Y. Yuan, J. Fang, Q. Wang, “Online anomaly detection in crowd scenes via structure analysis”, IEEE Transactions on Cybernetics 45 (3) (2015) 548–561.
[4]T. Wang, H. Snoussi, “Detection of abnormal visual events via global optical flow orientation histogram”, IEEE Transactions on Information Forensics and Security 9 (6) (2014) 988–998.
[5]N. Dalal, B. Triggs, “Histograms of oriented gradients for human detection”, Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, Vol. 1, (2005), pp. 886–893.
[6]G. Zen, E. Ricci, ‘Earth mover’s prototypes: A convex learning approach for discovering activity patterns in dynamic scenes”, Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, IEEE, 2011, pp. 3225–3232.
[7]Z. Fu, W. Hu, T. Tan, “Similarity based vehicle trajectory clustering and anomaly detection”, Image Processing, 2005. ICIP 2005. IEEE International Conference, Vol. 2, IEEE, 2005, pp. II–602.
[8]Y. Cong, J. Yuan, J. Liu, “Sparse reconstruction cost for abnormal event detection”, Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, IEEE, 2011, pp. 3449– 3456.
[9]C. Lu, J. Shi, J. Jia, “Abnormal event detection at 150 fps in matlab”, Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 2720–2727.
[10]B. Zhao, L. Fei-Fei, E. P. Xing, “Online detection of unusual events in videos via dynamic sparse coding”, Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, IEEE, 2011, pp. 3313–3320.
[11]O. Boiman, M. Irani, “Detecting irregularities in images and in video”, Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, Vol. 1, IEEE, 2005, pp. 462–469.
[12]V. Saligrama, Z. Chen, “Video anomaly detection based on local statistical aggregates”, Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, IEEE, 2012, pp. 2112– 2119.
[13]R. Hamid, A. Johnson, S. Batta, A. Bobick, C. Isbell, G. Coleman, “Detection and explanation of anomalous activities: Representing activities as bags of event n-grams”, Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, Vol. 1, IEEE, 2005, pp. 1031–1038.
[14]J. Kim, K. Grauman, “Observe locally, infer globally: a space time mrf for detecting abnormal activities with incremental updates”, Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, IEEE, 2009, pp. 2921–2928.
[15]D. Zhang, D. Gatica-Perez, S. Bengio, I. “McCowan, Semisupervised adapted hmms for unusual event detection”, Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, Vol. 1, 2005, pp. 611– 618.
[16]E. L. Andrade, S. Blunsden, R. B. Fisher, “Modelling crowd scenes for event detection”, Pattern Recognition, 2006. ICPR 2006. 18th International Conference on, Vol. 1, IEEE, 2006, pp. 175–178.
[17]Y. Benezeth, P.-M. Jodoin, V. Saligrama, C. Rosenberger, “Abnormal events detection based on spatio-temporal cooccurences”, Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, IEEE, 2009, pp. 2458–2465.
[18]R. Mehran, A. Oyama, M. Shah, “Abnormal crowd behavior detection using social force model”, Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, IEEE, 2009, pp. 935–942.
[19]A. Adam, E. Rivlin, I. Shimshoni, D. Reinitz, “Robust real-time unusual event detection using multiple fixed-location monitors”, IEEE transactions on pattern analysis and machine intelligence 30 (3) (2008) 555–560.
[20]S. Wu, B. E. Moore, M. Shah, “Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes”, Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, IEEE, 2010, pp. 2054–2060.
[21]X. Cui, Q. Liu, M. Gao, D. N. Metaxas, “Abnormal detection using interaction energy potentials”, Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, IEEE, 2011, pp. 3161–3167.
[22]M. H. Sharif, C. Djeraba, “An entropy approach for abnormal activities detection in video streams”, Pattern recognition 45 (7) (2012) 2543–2561.
[23]J. Kwon, K. M. Lee, “A unified framework for event summarization and rare event detection from multiple views”, IEEE transactions on pattern analysis and machine intelligence 37 (9) (2015) 1737–1750.
[24]X. Gu, J. Cui, Q. Zhu, “Abnormal crowd behavior detection by using the particle entropy”, Optik-International Journal for Light and Electron Optics 125 (14) (2014) 3428–3433.
[25]M. H. Sharif, S. Uyaver, C. Djeraba, “Crowd behavior surveillance using bhattacharyya distance metric”, International Symposium Computational Modeling of Objects Represented in Images, Springer, 2010, pp. 311–323.
[26]R. Goshorn, D. Goshorn, J. Goshorn, L. Goshorn, “Abnormal behavior-detection using sequential syntactical classification in a network of clustered cameras”, Distributed Smart Cameras, 2008. ICDSC 2008. Second ACM/IEEE International Conference on, IEEE, 2008, pp. 1–10.
[27]J. L. Barron, D. J. Fleet, S. S. Beauchemin, T. Burkitt, “Performance of optical flow techniques”, Computer Vision and Pattern Recognition, 1992. Proceedings CVPR’92., 1992 IEEE Computer Society Conference on, IEEE, 1992, pp. 236–242.
[28]W. Xiong, L. Zhang, B. Du, D. Tao, “Combining local and global: Rich and robust feature pooling for visual recognition”, Pattern Recognition 62 (2017) 225–235.
[29]Q. Leng, H. Qi, J. Miao, W. Zhu, G. Su, “One-class classification with extreme learning machine”, Mathematical problems in engineering 2015.
[30]UMN, Unusual event datasets of university of minesota. URL http://mha.cs.umn.edu/Movies/Crowd-Activity-All.avi.
[31]PETS, Pets2009 benchmark dataset. URL http://cs.binghamton.edu/~mrldata/pets2009
[32]C. Lu, J. Shi, J. Jia, Abnormal event detection at 150 fps in matlab, in: Proceedings of the IEEE international conference on computer vision, 2013, pp. 2720–2727. URL http://www.cse.cuhk.edu.hk/leojia/projects/detect abnormal/dataset.html
[33]V. Kaltsa, A. Briassouli, I. Kompatsiaris, L. J. Hadjileontiadis, M. G. Strintzis, “Swarm intelligence for detecting interesting events in crowded environments”, IEEE transactions on image processing 24 (7) (2015) 2153–2166.
[34]Y. Shi, Y. Gao, R. Wang, “Real-time abnormal event detection in complicated scenes”, Pattern Recognition (ICPR), 2010 20th International Conference on, IEEE, 2010, pp. 3653–3656.