It is essential to build the effective appearance model for object tracking in computer vision. Most object trackers can be roughly divided into two categories according to the appearance model: the bounding box model and the patch model. The bounding box model cannot handle shape deformation and occlusion of the non-rigid moving object effectively. The patch model is prone to be disturbed by complex backgrounds. In this paper, we propose a robust multi-structured-patch appearance model to represent the target for object tracking. The proposed appearance model is aimed to exploit and identify reliable patches that can be tracked effectively through the whole tracking process. According to attention mechanism in biological vision system, a coarse-to-fine strategy is usually used to search the target. Therefore, the proposed appearance model is represented by robust patches in different sizes, in which the bigger patches search the rough region of the target and the smaller patches estimate the accurate location. Experimental results on OTB100 dataset show that the proposed method outperforms state-of-the-art trackers.

对于计算机视觉中的对象跟踪，建立有效的外观模型至关重要。根据外观模型，大多数对象跟踪器可以大致分为两类：边界框模型和补丁模型。边界框模型不能有效地处理非刚性运动对象的形状变形和遮挡。补丁模型容易受到复杂背景的干扰。在本文中，我们提出了一个健壮的多结构补丁外观模型来表示目标跟踪目标。提出的外观模型旨在开发和识别可在整个跟踪过程中有效跟踪的可靠补丁。根据生物视觉系统中的注意力机制，通常采用从粗到精的策略来搜索目标。因此，提出的外观模型由大小不同的健壮补丁表示，其中较大的补丁搜索目标的粗糙区域，而较小的补丁估计准确的位置。在OTB100数据集上的实验结果表明，该方法优于最新的跟踪器。