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Visual abnormal behavior detection based on trajectory sparse reconstruction analysis
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AI
DOI：10.1016/j.neucom.2012.03.040.png)
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Abstract
En 中文
Abnormal behavior detection has been one of the most important research branches in intelligent video content analysis. In this paper, we propose a novel abnormal behavior detection approach by introducing trajectory sparse reconstruction analysis (SRA). Given a video scenario, we collect trajectories of normal behaviors and extract the control point features of cubic B-spline curves to construct a normal dictionary set, which is further divided into Route sets. On the dictionary set, sparse reconstruction coefficients and residuals of a test trajectory to the Route sets can be calculated with SRA. The minimal residual is used to classify the test behavior into a normal behavior or an abnormal one. SRA is solved by L1-norm minimization, leading to that a few of dictionary samples are used when reconstructing a behavior trajectory, which guarantees that the proposed approach is valid even when the dictionary set is very small. Experimental results with comparisons show that the proposed approach improves the state-of-the-art. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.
Keywords:
Abnormal behavior detection
Trajectory representation
Sparse reconstruction analysis
L1-norm minimization