Abstract
Aiming at the problem that fast discriminant scale-space tracking (FDSST) algorithm cannot solve the tracking problem of the agent target reappearing after a long-time and large-area occlusion, an improved FDSST algorithm based on Kalman filter is proposed. Firstly, the agent image sequence is labeled under FDSST architecture; Then, the maximum response value and average peak correlation energy are introduced as the occlusion judgment criteria. When the occlusion leads to the tracking failure, the Kalman filter is used to evaluate and predict the location of the agent, and the tracking of the agent target is realized combined with the incomplete information of FDSST; Finally, compared with the original FDSST algorithm, the improved FDSST algorithm is verified to get better performance by evaluating the data sets under four different attributes: occlusion, scale transformation, fast motion and motion blur in the online tracking benchmark (OTB) and the agent image sequence on the CoppeliaSim simulation platform.
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Thank Zi Ma for his financial support and help for this project.
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Wang, G., Ren, W., Huang, J., Liu, Y. (2022). An Improved Algorithm for Occlusion Tracking Based on FDSST and Kalman Filtering. In: S. Shmaliy, Y., Abdelnaby Zekry, A. (eds) 6th International Technical Conference on Advances in Computing, Control and Industrial Engineering (CCIE 2021). CCIE 2021. Lecture Notes in Electrical Engineering, vol 920. Springer, Singapore. https://doi.org/10.1007/978-981-19-3927-3_16
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DOI: https://doi.org/10.1007/978-981-19-3927-3_16
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