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Tracking topology structure adaptively with deep neural networks

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Abstract

Object tracking still remains challenging in computer vision because of the severe object variation, e.g., deformation, occlusion, and rotation. To handle the object variation and achieve robust object tracking performance, we propose a novel relationship-based tracking algorithm using neural networks in this paper. Compared with existing approaches in the literature, our method assumes the targeted object to be consisted of several parts and considers the evolution of the topology structure among these parts. After training a candidate neural network for predicting the probable areas each part may locate at in the successive frame, we then design a novel collaboration neural network to determine the precise area each part will locate at with account for the topology structure among these individual parts, which is learned from their historical physical locations during online tracking process. Experimental results show that the proposed method outperforms state-of-the-art trackers on a benchmark dataset, yielding the significant improvements in accuracy on high-distorted sequences.

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Acknowledgements

This work was supported by the Key Program of National Natural Science Foundation of China (61432012, U1435213). The authors would like to thank the anonymous reviewers for their valuable comments and suggestions that significantly improve the quality of this paper.

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Authors and Affiliations

  1. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, People’s Republic of China

    Xueying Shi & Zhang Yi

  2. Department of Computer Science and Engineering, Chinese University of Hong Kong, Hongkong, People’s Republic of China

    Guangyong Chen & Pheng Ann Heng

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  1. Xueying Shi
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  2. Guangyong Chen
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  3. Pheng Ann Heng
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  4. Zhang Yi
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Correspondence to Zhang Yi.

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Shi, X., Chen, G., Heng, P.A. et al. Tracking topology structure adaptively with deep neural networks. Neural Comput & Applic 30, 3317–3326 (2018). https://doi.org/10.1007/s00521-017-2906-y

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