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Structured Siamese Network for Real-Time Visual Tracking

  • Yunhua Zhang
  • Lijun Wang
  • Jinqing Qi
  • Dong Wang
  • Mengyang Feng
  • Huchuan Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11213)

Abstract

Local structures of target objects are essential for robust tracking. However, existing methods based on deep neural networks mostly describe the target appearance from the global view, leading to high sensitivity to non-rigid appearance change and partial occlusion. In this paper, we circumvent this issue by proposing a local structure learning method, which simultaneously considers the local patterns of the target and their structural relationships for more accurate target tracking. To this end, a local pattern detection module is designed to automatically identify discriminative regions of the target objects. The detection results are further refined by a message passing module, which enforces the structural context among local patterns to construct local structures. We show that the message passing module can be formulated as the inference process of a conditional random field (CRF) and implemented by differentiable operations, allowing the entire model to be trained in an end-to-end manner. By considering various combinations of the local structures, our tracker is able to form various types of structure patterns. Target tracking is finally achieved by a matching procedure of the structure patterns between target template and candidates. Extensive evaluations on three benchmark data sets demonstrate that the proposed tracking algorithm performs favorably against state-of-the-art methods while running at a highly efficient speed of 45 fps.

Keywords

Tracking Deep learning Siamese network 

Notes

Acknowledgement

This work was supported by the Natural Science Foundation of China under Grant 61725202, 61751212, 61771088, 61632006 and 91538201.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Information and Communication EngineeringDalian University of TechnologyDalianChina

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