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Intelligent abnormal behavior detection using double sparseness method

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Abstract

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.

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Acknowledgments

This work described in this paper was supported by the National Key Research and Development Program of China (grant no. 2016YFB0501805) and the Project of Research and Integrated Demonstration on the Technology of Unmanned Driving and Autonomous Operation of Agricultural Machinery (grant no. Z201100008020008).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Henan University, Zhengzhou, China

    Huiyu Mu

  2. College of Information and Electrical Engineering, China Agricultural University, Beijing, China

    Ruizhi Sun, Zeqiu Chen & Jia Qin

  3. Scientific Research Base for Integrated Technologies of Precision Agriculture, the Ministry of Agriculture, Beijing, China

    Ruizhi Sun

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  1. Huiyu Mu
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Correspondence to Ruizhi Sun.

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Mu, H., Sun, R., Chen, Z. et al. Intelligent abnormal behavior detection using double sparseness method. Appl Intell 53, 7728–7740 (2023). https://doi.org/10.1007/s10489-022-03903-8

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