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A parallel and robust object tracking approach synthesizing adaptive Bayesian learning and improved incremental subspace learning

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Abstract

This paper presents a novel tracking algorithm which integrates two complementary trackers. Firstly, an improved Bayesian tracker(B-tracker) with adaptive learning rate is presented. The classification score of B-tracker reflects tracking reliability, and a low score usually results from large appearance change. Therefore, if the score is low, we decrease the learning rate to update the classifier fast so that B-tracker can adapt to the variation and vice versa. In this way, B-tracker is more suitable than its traditional version to solve appearance change problem. Secondly, we present an improved incremental subspace learning method tracker(S-tracker). We propose to calculate projected coordinates using maximum posterior probability, which results in a more accurate reconstruction error than traditional subspace learning tracker. Instead of updating at every time, we present a stop-strategy to deal with occlusion problem. Finally, we present an integrated framework(BAST), in which the pair of trackers run in parallel and return two candidate target states separately. For each candidate state, we define a tracking reliability metrics to measure whether the candidate state is reliable or not, and the reliable candidate state will be chosen as the target state at the end of each frame. Experimental results on challenging sequences show that the proposed approach is very robust and effective in comparison to the state-of-the-art trackers.

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Acknowledgements

This paper is supported by the National Natural Science Foundation of China (Grant No. 61472289) and the National Key Research and Development Project of China (2016YFC0106305).

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Authors and Affiliations

  1. State Key Laboratory of Software Engineering, School of Computer Science, Wuhan University, Wuhan, 430072, China

    Kang Li, Fazhi He, Haiping Yu & Xiao Chen

  2. School of Computer Science and Information Engineering, Hubei University, Wuhan, 430062, China

    Kang Li

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  1. Kang Li
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  2. Fazhi He
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  3. Haiping Yu
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Corresponding author

Correspondence to Fazhi He.

Additional information

Kang Li received the MS degree in Computer Science from Central China Normal University, China in 2012. He is currently a PhD candidate in the Wuhan University, school of computer science. His research interests are pattern recognition, image processing, and computer vision.

Fazhi He received PhD degree from Wuhan University of Technology. Now he is a professor in School of Computer, Wuhan University, China. His research interests are computer graphics, computer-aided design, image processing and computer supported cooperative work.

Haiping Yu received the MS degree in Wuhan University of Science and Technology, China in 2005. She is currently a PhD candidate in the Wuhan University, school of computer science. Her research interests are medical image processing, computer vision and social recommendation.

Xiao Chen received the MS degree in Computer Science from Three Gorges University, China in 2010. He is currently a PhD candidate in the Wuhan University, school of computer science. His research interests are machine learning, image matting, and computer vision.

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Li, K., He, F., Yu, H. et al. A parallel and robust object tracking approach synthesizing adaptive Bayesian learning and improved incremental subspace learning. Front. Comput. Sci. 13, 1116–1135 (2019). https://doi.org/10.1007/s11704-018-6442-4

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