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  1. Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream (ARTEMIS '13)
  2. Abnormal crowd behavior detection and localization using maximum sub-sequence search
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On improving the robustness of variational optical flow against illumination changes
A non-parametric unsupervised approach for content based image retrieval and clustering
Abnormal crowd behavior detection and localization using maximum sub-sequence search
Hand gesture recognition with depth data
Warping trajectories for video synchronization
Behavior recognition from video based on human constrained descriptor and adaptable neural networks
Nobody likes Mondays: foreground detection and behavioral patterns analysis in complex urban scenes
Background modeling methods for visual detection of maritime targets
Domain transfer for person re-identification
Cross-domain traffic scene understanding by motion model transfer

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

Content Provider ACM Digital Library
Author Fang, Wen-Hsien Cheng, Kai-Wen Chen, Yie-Tarng
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.
Starting Page 49
Ending Page 58
Page Count 10
File Format PDF
ISBN 9781450323932
DOI 10.1145/2510650.2510655
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-10-21
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Video surveillance Localization Anomaly detection
Content Type Text
Resource Type Article
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