An improved background subtraction approach in target detection and tracking

Hao Lai; Yuesheng Zhu; Zhenming Nong

doi:10.1117/12.2052825

24 December 2013 An improved background subtraction approach in target detection and tracking

Hao Lai, Yuesheng Zhu, Zhenming Nong

Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90671S (2013) https://doi.org/10.1117/12.2052825
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom

Abstract

In this paper, a novel background subtraction approach is proposed to avoid stationary foreground objects being merged into the background in target detection and tracking, in which an improved background model is designed by using virtual frames and the blur can be attenuated with this model when an object moves again after it stays for a long time. Moreover, the proposed model is fused with the eigenbackgrounds to improve the environmental adaptability. Our experimental results indicate that the proposed approach enhances the performance of target detection and tracking in intelligent surveillance and is superior to some state-of-the-art methods according to the precision-recall measurement.

Citation Download Citation

Hao Lai, Yuesheng Zhu, and Zhenming Nong "An improved background subtraction approach in target detection and tracking", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90671S (24 December 2013); https://doi.org/10.1117/12.2052825

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