Laplacian Eigenmap With Temporal Constraints for Local Abnormality Detection in Crowded Scenes

This paper addresses the problem of detecting and localizing abnormal activities in crowded scenes. A spatiotemporal Laplacian eigenmap method is proposed to extract different crowd activities from videos. This is achieved by learning the spatial and temporal variations of local motions in an embedded space. We employ representatives of different activities to construct the model which characterizes the regular behavior of a crowd. This model of regular crowd behavior allows the detection of abnormal crowd activities both in local and global contexts and the localization of regions which show abnormal behavior. Experiments on the recently published data sets show that the proposed method achieves comparable results with the state-of-the-art methods without sacrificing computational simplicity.

[1]  W. Eric L. Grimson,et al.  Learning Semantic Scene Models by Trajectory Analysis , 2006, ECCV.

[2]  Mubarak Shah,et al.  Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  W. Eric L. Grimson,et al.  Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Brian C. Lovell,et al.  Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture , 2011, CVPR 2011 WORKSHOPS.

[5]  Mubarak Shah,et al.  Learning motion patterns in crowded scenes using motion flow field , 2008, 2008 19th International Conference on Pattern Recognition.

[6]  Robert Pless,et al.  Image spaces and video trajectories: using Isomap to explore video sequences , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[7]  Ko Nishino,et al.  Tracking with local spatio-temporal motion patterns in extremely crowded scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Pietro Perona,et al.  Self-Tuning Spectral Clustering , 2004, NIPS.

[9]  David C. Hogg,et al.  Learning the distribution of object trajectories for event recognition , 1996, Image Vis. Comput..

[10]  Nuno Vasconcelos,et al.  Anomaly detection in crowded scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Andrea Cavallaro,et al.  Event monitoring via local motion abnormality detection in non-linear subspace , 2010, Neurocomputing.

[12]  Yinghuan Shi,et al.  Real-Time Abnormal Event Detection in Complicated Scenes , 2010, 2010 20th International Conference on Pattern Recognition.

[13]  Yunqian Ma,et al.  Activity Representation in Crowd , 2008, SSPR/SPR.

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

[15]  Ricky J. Sethi,et al.  Modeling and Recognition of Complex Human Activities , 2011, Visual Analysis of Humans.

[16]  Tieniu Tan,et al.  A system for learning statistical motion patterns , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Nicolas Le Roux,et al.  Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering , 2003, NIPS.

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

[19]  Sridha Sridharan,et al.  Textures of optical flow for real-time anomaly detection in crowds , 2011, 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[20]  Mubarak Shah,et al.  Scene understanding by statistical modeling of motion patterns , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[21]  Yihong Gong,et al.  Incremental spectral clustering by efficiently updating the eigen-system , 2010, Pattern Recognit..

[22]  Amit K. Roy-Chowdhury,et al.  Modeling and recognition of complex multi-person interactions in video , 2010, MPVA '10.

[23]  Junsong Yuan,et al.  Sparse reconstruction cost for abnormal event detection , 2011, CVPR 2011.

[24]  W. Eric L. Grimson,et al.  Unsupervised Activity Perception by Hierarchical Bayesian Models , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Andrea Cavallaro,et al.  Video event segmentation and visualisation in non-linear subspace , 2009, Pattern Recognit. Lett..

[26]  Dorothy Ndedi Monekosso,et al.  Learning Video Manifold for Segmenting Crowd Events and Abnormality Detection , 2010, ACCV.

[27]  Ricky J. Sethi,et al.  Motion Pattern Analysis for Modeling and Recognition of Complex Human Activities , 2011 .

[28]  Amit K. Roy-Chowdhury,et al.  Wide Area Tracking in Single and Multiple Views , 2011, Visual Analysis of Humans.

[29]  Sharath Pankanti,et al.  Graph based event detection from realistic videos using weak feature correspondence , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

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

[31]  Mubarak Shah,et al.  Video Scene Understanding Using Multi-scale Analysis , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[32]  Dewen Hu,et al.  Incremental Laplacian eigenmaps by preserving adjacent information between data points , 2009, Pattern Recognit. Lett..

[33]  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.

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

[35]  William Brendel,et al.  Learning spatiotemporal graphs of human activities , 2011, 2011 International Conference on Computer Vision.

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

[37]  L. Kratz,et al.  Spatio-Temporal Motion Pattern Models of Extremely Crowded Scenes , 2011 .

[38]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[39]  Robert B. Fisher,et al.  Modelling Crowd Scenes for Event Detection , 2006, 18th International Conference on Pattern Recognition (ICPR'06).