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.

本文提出了一种基于 SiamFC 的 SPM-Tracker，通过两类不同的匹配阶段增强鲁棒性和判别能力，采用串并联结构连接两阶段，通过距离学习网络增强细匹配阶段的判别能力，在 OTB-100 和 VOT-16 测试中获得了比其他实时跟踪器更显著的性能。

SPM-Tracker：用于实时视觉对象跟踪的串并联匹配

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

Correlation filtering based tracking model has received lots of attention and
achieved great success in real-time tracking, however, the lost function in
current correlation filtering paradigm could not reliably response to the
appearance changes caused by occlusion and illumination variations. This study
intends to promote the robustness of the correlation filter learning. By
exploiting the anisotropy of the filter response, three sparsity related loss
functions are proposed to alleviate the overfitting issue of previous methods
and improve the overall tracking performance. As a result, three real-time
trackers are implemented. Extensive experiments in various challenging
situations demonstrate that the robustness of the learned correlation filter
has been greatly improved via the designed loss functions. In addition, the
study reveals, from an experimental perspective, how different loss functions
essentially influence the tracking performance. An important conclusion is that
the sensitivity of the peak values of the filter in successive frames is
consistent with the tracking performance. This is a useful reference criterion
in designing a robust correlation filter for visual tracking.

本研究提出了三种稀疏度相关的损失函数，通过利用滤波器响应的各向异性提高相关滤波学习的鲁棒性，从而实现了三种实时跟踪器。大量实验证明了所设计的损失函数显著提高了相关滤波的鲁棒性，并揭示了不同损失函数对跟踪性能的影响，从而为设计鲁棒的相关滤波器提供了实用参考标准。