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Mining Spatial-Temporal Similarity for Visual Tracking.
IEEE Transactions on Image Processing ( IF 10.6 ) Pub Date : 2020-06-03 , DOI: 10.1109/tip.2020.2981813
Yu Zhang , Xingyu Gao , Zhenyu Chen , Huicai Zhong , Hongtao Xie , Chenggang Yan

Correlation filter (CF) is a critical technique to improve accuracy and speed in the field of visual object tracking. Despite being studied extensively, most existing CF methods suffer from failing to make the most of the inherent spatial-temporal prior of videos. To address this limitation, as consecutive frames are eminently resemble in most videos, we investigate a novel scheme to predict targets’ future state by exploiting previous observations. Specifically, in this paper, we propose a prediction based CF tracking framework by learning the spatial-temporal similarity of consecutive frames for sample managing, template regularization, and training response pre-weighting. We model the learning problem theoretically as a novel objective and provide effective optimization algorithms to solve the learning task. In addition, we implement two CF trackers with different features. Extensive experiments are conducted on three popular benchmarks to validate our scheme. The encouraging results demonstrate that the proposed scheme can significantly boost the accuracy of CF tracking, and the two trackers achieve competitive performances against state-of-the-art trackers. We finally present a comprehensive analysis on the efficacy of our proposed method and the efficiency of our trackers to facilitate real-world visual tracking applications.

中文翻译:

挖掘视觉跟踪的时空相似性。

相关滤波器(CF)是提高视觉对象跟踪领域的准确性和速度的一项关键技术。尽管进行了广泛的研究,但是大多数现有的CF方法都无法充分利用视频的固有时空先验。为了解决此限制,因为大多数视频中都非常类似于连续帧,所以我们研究了一种通过利用先前的观察来预测目标的未来状态的新颖方案。具体而言,在本文中,我们通过学习连续帧的时空相似性以进行样本管理,模板正则化和训练响应预加权,提出了一种基于预测的CF跟踪框架。我们在理论上将学习问题建模为一个新目标,并提供有效的优化算法来解决学习任务。此外,我们实现了两个具有不同功能的CF跟踪器。在三个流行的基准上进行了广泛的实验,以验证我们的方案。令人鼓舞的结果表明,该方案可以显着提高CF跟踪的准确性,并且这两种跟踪器都可以与最先进的跟踪器取得竞争性能。最后,我们对提出的方法的有效性和跟踪器的效率进行了全面的分析,以促进现实世界中的视觉跟踪应用。
更新日期:2020-08-08
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