Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
406467 | Neurocomputing | 2014 | 9 Pages |
Abstract
In this paper, we present a novel approach for video-anomaly detection in crowded and complicated scenes. The proposed approach detects anomalies based on a hierarchical activity-pattern discovery framework, comprehensively considering both global and local spatio-temporal contexts. The discovery is a coarse-to-fine learning process with unsupervised methods for automatically constructing normal activity patterns at different levels. A unified anomaly energy function is designed based on these discovered activity patterns to identify the abnormal level of an input motion pattern. We demonstrate the effectiveness of the proposed method on the UCSD anomaly-detection datasets and compare the performance with existing work.
Keywords
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Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Dan Xu, Rui Song, Xinyu Wu, Nannan Li, Wei Feng, Huihuan Qian,