IEEE Trans Image Process. 2018 Mar;27(3):1282-1296. doi: 10.1109/TIP.2017.2779275. Epub 2017 Dec 4.
In this paper, a novel spatial-temporal locality is proposed and unified via a discriminative dictionary learning framework for visual tracking. By exploring the strong local correlations between temporally obtained target and their spatially distributed nearby background neighbors, a spatial-temporal locality is obtained. The locality is formulated as a subspace model and exploited under a unified structure of discriminative dictionary learning with a subspace structure. Using the learned dictionary, the target and its background can be described and distinguished effectively through their sparse codes. As a result, the target is localized by integrating both the descriptive and the discriminative qualities. Extensive experiments on various challenging video sequences demonstrate the superior performance of proposed algorithm over the other state-of-the-art approaches.
本文提出了一种新的时空局部性，并通过判别式字典学习框架进行统一，用于视觉跟踪。通过探索目标在时间上的获取与其空间分布的附近背景邻居之间的强局部相关性，得到时空局部性。该局部性被表述为子空间模型，并在具有子空间结构的判别式字典学习的统一结构下进行利用。使用学习到的字典，可以通过稀疏码有效地描述和区分目标及其背景。因此，通过整合描述性和判别性质量，目标被定位。在各种具有挑战性的视频序列上的广泛实验表明，所提出的算法优于其他最先进的方法。
