SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking

Guangting Wang, Chong Luo, Zhiwei Xiong, Wenjun Zeng; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 3643-3652

Abstract


The greatest challenge facing visual object tracking is the simultaneous requirements on robustness and discrimination power. In this paper, we propose a SiamFC-based tracker, named SPM-Tracker, to tackle this challenge. The basic idea is to address the two requirements in two separate matching stages. Robustness is strengthened in the coarse matching (CM) stage through generalized training while discrimination power is enhanced in the fine matching (FM) stage through a distance learning network. The two stages are connected in series as the input proposals of the FM stage are generated by the CM stage. They are also connected in parallel as the matching scores and box location refinements are fused to generate the final results. This innovative series-parallel structure takes advantage of both stages and results in superior performance. The proposed SPM-Tracker, running at 120fps on GPU, achieves an AUC of 0.687 on OTB-100 and an EAO of 0.434 on VOT-16, exceeding other real-time trackers by a notable margin.

Related Material


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[bibtex]
@InProceedings{Wang_2019_CVPR,
author = {Wang, Guangting and Luo, Chong and Xiong, Zhiwei and Zeng, Wenjun},
title = {SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}