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Background subtraction via regional multi-feature-frequency model in complex scenes

  • Application of soft computing
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Abstract

Although a wide variety of background subtraction methods has been proposed in recent years, none has been able to fully address multi-scale moving objects and dynamic background in real surveillance tasks. In this paper, a novel and effective background subtraction method, named regional multi-feature-frequency (RMFF), is proposed to detect multi-scale moving objects under dynamic background. Unlike many existing methods construct background model using simple multi-feature combinations, RMFF exploits the spatiotemporal cues of multi-feature as well as superpixels at each scale, thus allowing for more robust information to be exploited for background modeling. Specifically, the spatial relationship between pixels in a neighborhood and the frequencies of features over time are first exploited, enabling accurate detection of moving objects while ignoring most dynamic background changes. Then, the use of multi-scale superpixels for exploiting the structural information existing in real-world scenes further enhances robustness to multi-scale objects and environmental variations. Finally, an adaptive strategy is employed to dynamically adjust the foreground/background segmentation threshold for each region without user intervention. This adaptive threshold is defined for each region separately, and can adjust dynamically based on continuous monitoring of the background changes, thereby effectively reducing potential segmentation noise. Experiments on the 2014 version of the ChangeDetection.net dataset demonstrate that the proposed method outperforms the 12 state-of-the-art algorithms in terms of overall F-Measure and performs effectively in many complex scenes. Consequently, it is verified that the developed approach is feasible and useful for robust application in practical video surveillance.

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Data availability

All data included in this study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to acknowledge that this work was supported in part by the National Natural Science Foundation of China under Grant 619770022, in part by the Foundation of Education Bureau of Hunan Province under Grant 21B0590, 20K062, in part by the Hunan Graduate Student Research Innovation Project under Grant CX20211189, in part by College Students’ Innovation and Entrepreneurship Training Program S202110543052, and in part by the Youth Project of Natural Science and Technology Foundation of Jiangsu Province under Grant BK20210868.

Funding

Funding is provided by National Natural Science Foundation of China (Grant No. 619770022), Foundation of Education Bureau of Hunan Province under Grant (Grant No. 21B0590), Education Department of Hunan Province (Grant No. 20K062), Hunan Graduate Student Research Innovation Project (Grant No. CX20211189), and College Students’ Innovation and Entrepreneurship Training Program (Grant No. S202110543052).

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Authors and Affiliations

  1. Hunan Institute of Science and Technology, Yueyang, China

    Qi Qi, Xin Yu, Ping Lei, Wei He, Guoyun Zhang, Jianhui Wu & Bing Tu

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  1. Qi Qi
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  2. Xin Yu
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Correspondence to Wei He.

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Qi, Q., Yu, X., Lei, P. et al. Background subtraction via regional multi-feature-frequency model in complex scenes. Soft Comput 27, 15305–15318 (2023). https://doi.org/10.1007/s00500-023-07955-x

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