Object tracking relies on a recursive search technique around the previous target location, concurrently learning the target appearance in each frame. A failure in any frame causes a drift from its optimal target path. Thus, obtaining highly confident search regions is essential in each frame. Motivated by the strong localization property of segmented object masks, the proposed method introduces instance segmentation as an attention mechanism in object tracking framework. The core contribution of this paper is threefold: (i) a region proposal module (RPM) based on instance segmentation to focus on search proposals having a high probability of being the target, (ii) a target localization module (TLM) to localize the final target using a correlation filter and (iii) a domain adaptation technique in both RPM and TLM modules to incorporate target specific knowledge and strong discrimination ability. Extensive experimental evaluation on three benchmark datasets demonstrate a significant average gain of 2.47% in precision, 2.55% in AUC score and 2.15% in overlap score in comparison with recent competing trackers.

对象跟踪依赖于围绕先前目标位置的递归搜索技术，同时学习每一帧中的目标外观。任何帧中的失败都会导致偏离其最佳目标路径。因此，在每一帧中获得高度可信的搜索区域是必不可少的。受分割对象掩码的强定位特性的启发，所提出的方法在对象跟踪框架中引入实例分割作为注意机制。本文的核心贡献有三点：（i）基于实例分割的区域提议模块（RPM），专注于成为目标概率高的搜索提议，(ii) 目标定位模块 (TLM) 使用相关滤波器定位最终目标和 (iii) RPM 和 TLM 模块中的域适应技术，以结合目标特定知识和强大的辨别能力。对三个基准数据集的广泛实验评估表明，与最近的竞争跟踪器相比，精确度平均提高了 2.47%，AUC 得分提高了 2.55%，重叠得分提高了 2.15%。