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.
Multiple object tracking using a dual‐attention network for autonomous driving
IET Intelligent Transport Systems ; 14 , 8 ; 842-848
2020-08-01
7 pages
Article (Journal)
Electronic Resource
English
novel MOT algorithm , traffic safety , popular MOT 17 benchmark , scene attention region , road safety , object detection , high‐level MOT algorithms , traffic scenes , road traffic , multiple object tracking , autonomous driving , attention modules , feature extraction , traffic engineering computing , parallel single object trackers , traffic road users , dual‐attention network , open problem , autonomous vehicles , novel detection refinement strategy , saliency detection method , object tracking , vulnerable road users , channel attention
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