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Learning regression and verification networks for long-term visual tracking

2018-09-12 09:13:48
Yunhua Zhang, Dong Wang, Lijun Wang, Jinqing Qi, Huchuan Lu

Abstract

In the long-term single object tracking task, the target moves out of view frequently. It is difficult to determine the presence of the target and re-search the target in the entire image. In this paper, we circumvent this issue by introducing a collaborative framework that exploits both matching mechanism and discriminative features to account for target identification and image-wide re-detection. Within the proposed collaborative framework, we develop a matching based regression module and a classification based verification module for long-term visual tracking. In the regression module, we present a regressor that conducts matching learning and copes with drastic appearance changes. In the verification module, we propose a classifier that filters out distractions efficiently. Compared to previous long-term trackers, the proposed tracker is able to track the target object more robustly in long-term sequences. Extensive experiments show that our algorithm achieves state-of-the-art results on several datasets.

Abstract (translated)

在长期单个对象跟踪任务中,目标经常移出视图。难以确定目标的存在并在整个图像中重新搜索目标。在本文中,我们通过引入一个协作框架来解决这个问题,该框架利用匹配机制和判别特征来解释目标识别和图像范围的重新检测。在提议的协作框架内,我们开发了基于匹配的回归模块和基于分类的验证模块,用于长期视觉跟踪。在回归模块中,我们提出了一个回归量,它可以进行匹配学习并应对剧烈的外观变化。在验证模块中,我们提出了一种有效过滤干扰的分类器。与之前的长期跟踪器相比,所提出的跟踪器能够在长期序列中更稳健地跟踪目标对象。大量实验表明,我们的算法在几个数据集上实现了最先进的结果。

URL

https://arxiv.org/abs/1809.04320

PDF

https://arxiv.org/pdf/1809.04320.pdf


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