Realtime Anomaly Detection Using Trajectory-Level Crowd Behavior Learning

We present an algorithm for realtime anomaly detection in low to medium density crowd videos using trajectorylevel behavior learning. Our formulation combines online tracking algorithms from computer vision, non-linear pedestrian motion models from crowd simulation, and Bayesian learning techniques to automatically compute the trajectory-level pedestrian behaviors for each agent in the video. These learned behaviors are used to segment the trajectories and motions of different pedestrians or agents and detect anomalies. We demonstrate the interactive performance on the PETS 2016 ARENA dataset as well as indoor and outdoor crowd video benchmarks consisting of tens of human agents.

[1]  Mubarak Shah,et al.  Identifying Behaviors in Crowd Scenes Using Stability Analysis for Dynamical Systems , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  L. Kratz,et al.  Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Dinesh Manocha,et al.  Efficient trajectory extraction and parameter learning for data-driven crowd simulation , 2015, Graphics Interface.

[4]  Xiaogang Wang,et al.  Coherent Filtering: Detecting Coherent Motions from Crowd Clutters , 2012, ECCV.

[5]  Soraia Raupp Musse,et al.  Using computer vision to simulate the motion of virtual agents , 2007, Comput. Animat. Virtual Worlds.

[6]  Dinesh Manocha,et al.  Interactive Crowd Content Generation and Analysis Using Trajectory-Level Behavior Learning , 2015, 2015 IEEE International Symposium on Multimedia (ISM).

[7]  Dinesh Manocha,et al.  Reciprocal n-Body Collision Avoidance , 2011, ISRR.

[8]  Xiaona Li,et al.  Simulating realistic crowd based on agent trajectories , 2013, Comput. Animat. Virtual Worlds.

[9]  Pierre-Marc Jodoin,et al.  Meta-tracking for video scene understanding , 2013, 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[10]  Bingbing Ni,et al.  Recognizing human group activities with localized causalities , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Dinesh Manocha,et al.  Realtime Multilevel Crowd Tracking Using Reciprocal Velocity Obstacles , 2014, 2014 22nd International Conference on Pattern Recognition.

[12]  Dinesh Manocha,et al.  Interactive and adaptive data-driven crowd simulation , 2016, 2016 IEEE Virtual Reality (VR).

[13]  Dinesh Manocha,et al.  REACH - Realtime crowd tracking using a hybrid motion model , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[14]  Xiaogang Wang,et al.  Scene-Independent Group Profiling in Crowd , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

[16]  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.

[17]  Soraia Raupp Musse,et al.  Crowd Analysis Using Computer Vision Techniques , 2010, IEEE Signal Processing Magazine.

[18]  Bingbing Ni,et al.  Crowded Scene Analysis: A Survey , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[19]  Ivan Laptev,et al.  Data-driven crowd analysis in videos , 2011, ICCV.