Multiple object tracking (MOT) remains an open and challenging problem for autonomous vehicles. Existing methods mainly ignore prior information from real traffic scenes. Here, the authors propose a novel MOT algorithm that considers traffic safety for vulnerable road users. The proposed method integrates two attention modules with a novel detection refinement strategy. Since skilled drivers pay more attention to pedestrians and cyclists, the authors employ a saliency detection method to extract scene attention region. Then, a detection refinement strategy achieved a good trade-off between parallel single object trackers and detection results. Channel attention can mine the most useful feature channel for traffic road users. In the end, the authors operate their method on the popular MOT 17 benchmark in comparison with other high-level MOT algorithms. The tracking results show that the proposed dual-attention network achieves the state-of-the-art performance.

中文翻译：

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使用双注意网络进行自动驾驶的多目标跟踪

对于自动驾驶汽车而言，多目标跟踪（MOT）仍然是一个开放且具有挑战性的问题。现有方法主要忽略真实交通场景中的先验信息。在这里，作者提出了一种新颖的MOT算法，该算法考虑了弱势道路使用者的交通安全。所提出的方法将两个注意力模块与一种新颖的检测细化策略集成在一起。由于熟练的驾驶员更加关注行人和骑自行车的人，因此作者采用了一种显着性检测方法来提取场景关注区域。然后，检测细化策略在并行的单个对象跟踪器和检测结果之间取得了良好的折衷。频道关注可以为交通道路用户挖掘最有用的功能频道。到底，与其他高级MOT算法相比，作者在流行的MOT 17基准上运行其方法。跟踪结果表明，提出的双注意力网络达到了最新的性能。