Abnormal crowd behavior detection and localization using maximum sub-sequence search

Authors:
Kai-Wen Cheng;Yie-Tarng Chen;Wen-Hsien Fang
Affiliations:
National Taiwan University of Science and Technology, Taipei, Taiwan Roc;National Taiwan University of Science and Technology, Taipei, Taiwan Roc;National Taiwan University of Science and Technology, Taipei, Taiwan Roc
Venue:
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
Year:
2013

Citing 9
Cited 0

Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Estimating the Support of a High-Dimensional Distribution

Neural Computation
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors

IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures

IEEE Transactions on Pattern Analysis and Machine Intelligence
Human detection using oriented histograms of flow and appearance

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Sparse reconstruction cost for abnormal event detection

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Optimal spatio-temporal path discovery for video event detection

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Video anomaly detection based on local statistical aggregates

CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel framework for anomaly event detection and localization in crowded scenes. We propose an anomaly detector that extends the Bayes classifier from multi-class to one-class classification to characterize normal events. We also propose a localization scheme for anomaly localization as a maximum subsequence problem in a video sequence. The maximum subsequence algorithm locates an anomaly event by discovering the optimal collection of successive patches with spatial proximity over time without prior knowledge of the size, start and end of the anomaly event. Our localization scheme can locate multiple occurrences of abnormal events in spite of noise. Experimental results on the well-established UCSD dataset show that the proposed framework significantly outperforms state-of-the-art methods up to 53.55% localization rate. This study concludes that the localization framework plays an important role in abnormal event detection.