Multimedia Tools and Applications

, Volume 76, Issue 5, pp 6263–6279 | Cite as

Anomaly detection based on spatio-temporal sparse representation and visual attention analysis

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Abstract

In this paper, we proposed a unified framework for anomaly detection and localization in crowed scenes. For each video frame, we extract the spatio-temporal sparse features of 3D blocks and generate the saliency map using a block-based center-surround difference operator. Two sparse coding strategies including off-line long-term sparse representation and on-line short-term sparse representation are integrated within our framework. Abnormality of each candidate is measured using bottom-up saliency and top-down fixation inference and further used to classify the frames into normal and anomalous ones by a binary classifier. Local abnormal events are localized and segmented based on the saliency map. In the experiments, we compared our method against several state-of-the-art approaches on UCSD data set which is a widely used anomaly detection and localization benchmark. Our method outputs competitive results with near real-time processing speed compared to state-of-the-arts.

Keywords

Sparse representation Anomaly detection Visual learning Visual attention model Fixation inference Anomaly localization ROC Independent component analysis Maximum a posterior 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHeilongjiangChina

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