Laser-based intelligent surveillance and abnormality detection in extremely crowded scenarios

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system that can perform robustly in the extremely crowded area has become an urgent need for public security. In this paper, we propose a novel laser-based system which can simultaneously perform the tracking, semantic scene learning and abnormality detection in the large and crowded environment. In our system, a novel abnormality detection model is proposed, and it considers and combines various factors that will influence human activity. Moreover, this model intensively investigate the relationship between pedestrians' social behaviors and their walking scenarios. We successfully applied the proposed system to the JR subway station of Tokyo, which can cover a 60×35m area, robustly track more than 180 targets at the same time and simultaneously perform the online semantic scene learning and abnormality detection with no human intervention.

[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]  S. Dai,et al.  Centrifugal force model for pedestrian dynamics. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Xuan Song,et al.  Tracking interacting targets with laser scanner via on-line supervised learning , 2008, 2008 IEEE International Conference on Robotics and Automation.

[4]  René Vidal,et al.  Online Clustering of Moving Hyperplanes , 2006, NIPS.

[5]  Ali Shahrokni,et al.  Video Activity Extraction and Reporting with Incremental Unsupervised Learning , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[6]  Shaogang Gong,et al.  Scene Segmentation for Behaviour Correlation , 2008, ECCV.

[7]  Tieniu Tan,et al.  Similarity based vehicle trajectory clustering and anomaly detection , 2005, IEEE International Conference on Image Processing 2005.

[8]  Ryosuke Shibasaki,et al.  A novel system for tracking pedestrians using multiple single-row laser-range scanners , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[9]  Dorin Comaniciu,et al.  Distribution Free Decomposition of Multivariate Data , 1998, Pattern Analysis & Applications.

[10]  Mubarak Shah,et al.  A Streakline Representation of Flow in Crowded Scenes , 2010, ECCV.

[11]  W. Eric L. Grimson,et al.  Trajectory analysis and semantic region modeling using a nonparametric Bayesian model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Zhouyu Fu,et al.  Semantic-Based Surveillance Video Retrieval , 2007, IEEE Transactions on Image Processing.

[13]  Xuan Song,et al.  An online approach: Learning-Semantic-Scene-by-Tracking and Tracking-by-Learning-Semantic-Scene , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  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.

[15]  Xuan Song,et al.  A novel laser-based system: Fully online detection of abnormal activity via an unsupervised method , 2011, 2011 IEEE International Conference on Robotics and Automation.

[16]  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.

[17]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[18]  Mubarak Shah,et al.  Abnormal crowd behavior detection using social force model , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[21]  Fei-Fei Li,et al.  Online detection of unusual events in videos via dynamic sparse coding , 2011, CVPR 2011.

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

[23]  François Brémond,et al.  Trajectory Based Activity Discovery , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.