A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Event Detection by Eigenvector Decomposition Using Object and Frame Features
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
Video-based event recognition: activity representation and probabilistic recognition methods
Computer Vision and Image Understanding - Special issue on event detection in video
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hidden Markov Models for Optical Flow Analysis in Crowds
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Vision and Applications
Trajectory classification using switched dynamical hidden Markov models
IEEE Transactions on Image Processing
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In this paper, we present a new approach for event detection of pedestrian interaction in crowded and cluttered scenes. Existing work is focused on the detection of an abnormal event in general or on the detection of specific simple events incorporating only up to two trajectories. In our approach, event detection in large groups of pedestrians is performed by exploiting motion interaction between pairs of pedestrians in a graph-based framework. Event detection is done by analyzing the temporal behaviour of the motion interaction with Hidden Markov Models (HMM). In addition, temporarily unsteady edges in the graph can be compensated by a HMM buffer which internally continues the HMM analysis even if the representing pedestrians depart from each other awhile. Experimental results show the capability of our graph-based approach for event detection by means of an image sequence in which pedestrians approach a soccer stadium.