In the environment perception stage of autonomous driving, vehicles need to track its surrounding objects quickly and accurately to avoid dangerous behaviors. Therefore, visual object tracking has important practical application value in autonomous driving system. However, the performance of most hierarchical convolutional feature trackers are limited by ignoring the complex environment of autonomous driving. In this paper, a novel Siamese Attention Network to explore the rich spatial and channel information of objects was proposed. Because of the lack of important information between the channel and the spatial position, the tracking performance is reduced by the challenges of illumination change and deformation. The spatial attention block and channel attention block focus on the importance of different spatial positions and channels, respectively. The effective fusion of the two makes our tracker achieve the state-of-the art performance of 0.300 in the EAO criterion of 2017, which exceeds the baseline by 5.7%.

在自动驾驶的环境感知阶段，车辆需要快速，准确地跟踪周围的物体，以避免危险的行为。因此，视觉目标跟踪在自动驾驶系统中具有重要的实际应用价值。但是，大多数分层卷积特征跟踪器的性能由于忽略了自动驾驶的复杂环境而受到限制。本文提出了一种新颖的暹罗注意力网络，用于探索物体的丰富空间和通道信息。由于通道和空间位置之间缺少重要信息，因此照明变化和变形的挑战降低了跟踪性能。空间注意力块和通道注意力块专注于不同空间位置和通道的重要性，分别。两者的有效融合使我们的追踪器在2017年的EAO标准中达到了0.300的最新性能，比基线高出5.7％。