Anomaly detection in crowded scenes

A novel framework for anomaly detection in crowded scenes is presented. Three properties are identified as important for the design of a localized video representation suitable for anomaly detection in such scenes: 1) joint modeling of appearance and dynamics of the scene, and the abilities to detect 2) temporal, and 3) spatial abnormalities. The model for normal crowd behavior is based on mixtures of dynamic textures and outliers under this model are labeled as anomalies. Temporal anomalies are equated to events of low-probability, while spatial anomalies are handled using discriminant saliency. An experimental evaluation is conducted with a new dataset of crowded scenes, composed of 100 video sequences and five well defined abnormality categories. The proposed representation is shown to outperform various state of the art anomaly detection techniques.

[1]  Ehud Rivlin,et al.  Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Solomon Kullback,et al.  Information Theory and Statistics , 1970, The Mathematical Gazette.

[3]  Nuno Vasconcelos,et al.  On the plausibility of the discriminant center-surround hypothesis for visual saliency. , 2008, Journal of vision.

[4]  Michal Irani,et al.  Detecting Irregularities in Images and in Video , 2005, ICCV.

[5]  Tianzhu Zhang,et al.  Learning semantic scene models by object classification and trajectory clustering , 2009, CVPR.

[6]  Nuno Vasconcelos,et al.  Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Mubarak Shah,et al.  Learning object motion patterns for anomaly detection and improved object detection , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Kristen Grauman,et al.  Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates , 2009, CVPR.

[9]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[10]  Nuno Vasconcelos,et al.  Background subtraction in highly dynamic scenes , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[12]  Nuno Vasconcelos,et al.  Probabilistic kernels for the classification of auto-regressive visual processes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Mubarak Shah,et al.  A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  John R. Hershey,et al.  Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[15]  Louis Kratz,et al.  Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models , 2009, CVPR.

[16]  Hai Tao,et al.  Counting Pedestrians in Crowds Using Viewpoint Invariant Training , 2005, BMVC.

[17]  Stephen J. Maybank,et al.  Fusion of Multiple Tracking Algorithms for Robust People Tracking , 2002, ECCV.

[18]  Ramin Mehran,et al.  Abnormal crowd behavior detection using social force model , 2009, CVPR.

[19]  Mubarak Shah,et al.  Floor Fields for Tracking in High Density Crowd Scenes , 2008, ECCV.

[20]  S. Kullback,et al.  Information Theory and Statistics , 1959 .

[21]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[22]  Stefano Soatto,et al.  Dynamic Textures , 2003, International Journal of Computer Vision.

[23]  Nuno Vasconcelos,et al.  Decision-Theoretic Saliency: Computational Principles, Biological Plausibility, and Implications for Neurophysiology and Psychophysics , 2009, Neural Computation.