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Online bionic visual siamese tracking based on mixed time-event triggering mechanism

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Abstract

Existing Siamese-based trackers deal with target deformation and occlusion by introducing online updates. However, these trackers still suffer from model drift due to the cumulative error in tracking results and the lack of a suitable model update strategy. To solve this problem, we propose an online bionic visual siamese tracking framework based on the mixed time-event triggering mechanism. In which, the bionic vision network introduces the receptive field block and the blurpool, which improve the quality of feature extraction while maintaining the translational invariance of the convolutional neural network. The former uses dilated convolution kernels with different dilation rates to fuse depth features, which effectively increases the receptive field of the network. The latter uses low-pass filtering to anti-alias before downsampling, reducing the negative impact of the downsampling operation on the generalization ability of the network. In addition, to enable the model to effectively capture target appearance variations, a template update strategy with the mixed time-event triggering mechanism is designed. The strategy evaluates the quality of tracking results via a quality assessment model, guided by the mixed time-event triggering mechanism to adaptively weighted fusion of fixed and mutative templates. Numerous experiments conducted on OTB100, VOT2016, VOT2018, UAV123, GOT-10k benchmarks show that the proposed tracker outperforms the baseline tracker and achieves state-of-the-art performance.

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Data Availability

The raw/processed data required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant (61873246, 62072416, 62006213, 62102373), Program for Science & Technology Innovation Talents in Universities of Henan Province (21HASTIT028), Natural Science Foundation of Henan (202300410495), Key Scientific Research Projects of Colleges and Universities in Henan Province (21A120010).

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Dongfeng Road, Zhengzhou, 450002, Henan Province, People’s Republic of China

    Huanlong Zhang, Zhuo Zhang & Jiapeng Zhang

  2. Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Yatai Road, Huzhou, 313001, Zhejiang Province, People’s Republic of China

    Yanchun Zhao

  3. China Tobacco Henan Industrial CO.,LTD, Henan, People’s Republic of China

    Miao Gao

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  1. Huanlong Zhang
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Correspondence to Huanlong Zhang.

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Zhang, H., Zhang, Z., Zhang, J. et al. Online bionic visual siamese tracking based on mixed time-event triggering mechanism. Multimed Tools Appl 82, 15199–15222 (2023). https://doi.org/10.1007/s11042-022-13930-9

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