Some Siamese-based trackers use temporal context prior as structural constraint to suppress background distractors. However, due to the lack of contour recognition, it is difficult to obtain a better performance. In order to address this issue, we propose a structural pixel-wise target attention strategy for robust object tracking with memory model. Firstly, a pixel-wise target attention model is constructed for evaluating the probability that the pixel belongs to the target, which can effectively discriminate the target boundary so as to highlight the target area. Meanwhile, structural information is used to solve pixel-wise distractors, which is combined with complementary pixel-wise label constraints to obtain a structural pixel-wise target attention model. This attention mechanism can improve the confidence of the final response map and achieve more reliable target location. Secondly, a memory model is learned using the highly reliable memory pattern for providing high-quality training samples for updating pixel-wise target attention model. Benefiting from this method, our method realizes the pixel-wise target attention model to adapt to the variation of the target while preventing the background noise, thus improving the discriminability of the model. Finally, the structural pixel-wise target attention mechanism and memory model are integrated into the Siamese-based tracking framework, which shows better merit for robust object tracking. Extensive experiments on multiple tracking benchmarks show that our approach achieves excellent performance in various challenging target tracking tasks.

一些基于连体的跟踪器使用时间上下文先验作为结构约束来抑制背景干扰。然而，由于缺乏轮廓识别，很难获得更好的性能。为了解决这个问题，我们提出了一种结构化像素级目标注意策略，用于使用记忆模型进行稳健的对象跟踪。首先，构建一个逐像素的目标注意力模型，用于评估像素属于目标的概率，可以有效区分目标边界，从而突出目标区域。同时，结构信息用于解决逐像素干扰项，结合互补的逐像素标签约束，获得结构逐像素目标注意模型。这种注意力机制可以提高最终响应图的置信度，实现更可靠的目标位置。其次，使用高度可靠的记忆模式学习记忆模型，为更新像素级目标注意力模型提供高质量的训练样本。受益于该方法，我们的方法实现了逐像素的目标注意模型，在防止背景噪声的同时适应目标的变化，从而提高了模型的可辨别性。最后，将结构像素级目标注意机制和记忆模型集成到基于 Siamese 的跟踪框架中，这对于稳健的对象跟踪显示出更好的优点。在多个跟踪基准上的大量实验表明，我们的方法在各种具有挑战性的目标跟踪任务中取得了出色的性能。