Motion-based unusual event detection in human crowds

Authors:
Duan-Yu Chen;Po-Chung Huang
Affiliations:
Department of Electrical Engineering, Yuan-Ze University, Taiwan;Department of Electrical Engineering, Yuan-Ze University, Taiwan
Venue:
Journal of Visual Communication and Image Representation
Year:
2011

Citing 6
Cited 3

W4: Real-Time Surveillance of People and Their Activities

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
Modelling Crowd Scenes for Event Detection

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
An iterative image registration technique with an application to stereo vision

IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Detecting contextual anomalies of crowd motion in surveillance video

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing

Video object segmentation in rainy situations based on difference scheme with object structure and color analysis

Journal of Visual Communication and Image Representation
Joint segmentation of collectively moving objects using a bag-of-words model and level set evolution

Pattern Recognition
A multi-resolution approach for massively-parallel hardware-friendly optical flow estimation

Journal of Visual Communication and Image Representation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Analyzing human crowds is an important issue in video surveillance and is a challenging task due to their nature of non-rigid shapes. In this paper, optical flows are first estimated and then used for a clue to cluster human crowds into groups in unsupervised manner using our proposed method of adjacency-matrix based clustering (AMC). While the clusters of human crowds are obtained, their behaviors with attributes, orientation, position and crowd size, are characterized by a model of force field. Finally, we can predict the behaviors of human crowds based on the model and then detect if any anomalies of human crowd(s) present in the scene. Experimental results obtained by using extensive dataset show that our system is effective in detecting anomalous events for uncontrolled environment of surveillance videos.