Real-Time Multiple Object Tracking with Discriminative Features
Zhenyu Weng, Yuesheng Zhu, Zhiping Lin, Haizhou Li · 2020
Tracking-by-detection methods track multiple objects by detecting the objects of interest in each frame and associating the detected objects with the tracks. By allowing object detection and appearance embedding to be learned in a shared network, recent tracking-by-detection methods can implement the tracking task in real time with the power of deep neural networks. However, they just focus on the detection stage and do not take advantage of the embedding features well in the association stage. In this paper, we exploit the discriminative embedding features in the association stage to improve the tracking performance. By combing the embedding features with the bounding boxes to associate the detected objects with the tracks, the number of identity switches during tracking can be reduced. Further, after associating the detected objects with the tracks, the embedding feature of each track is not only updated according to the associated object, but also learned to distinguish the similar detected objects. The experiments show that our method can achieve competitive tracking performance in real time compared to the state-of-the-art tracking methods.