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Intelligent abnormal behavior detection using double sparseness method
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AI
DOI：10.1007/s10489-022-03903-8.png)
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Abstract
En 中文
Intelligent detection of abnormal behaviors meets the need of engineering applications for identifying anomalies and alerting operators. However, most existing methods tackle the high-dimensional sequential video data with key frame extraction, which ignore the redundancy effect of inter- and intra- video frames. In this paper, a novel Abnormal Detection method based on double sparseness LSSVMoc (AD_LSSVMoc) is proposed, which combine both sample (i.e. frame) selection and feature selection simultaneously in a uniform sparse model. For the feature extraction, both handcrafted features and learned features are aggregated into effective descriptors. To achieve feature selection and sample selection, a improved LSSVMoc is proposed with sparse primal and dual optimization strategy, and alternating direction method of multipliers is used to solve the constrained linear equations problem raised in AD_LSSVMoc. Experiments show that the proposed AD_LSSVMoc method achieves a competitive detection performance and high detecting speed compared to state-of-the-art methods.
Keywords:
Abnormal detection
Feature selection
Sample selection
Least squares one-class SVM
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