Abstract:
Aiming at the shortcomings in multi-object tracking in complex scenes, e.g., high object identification switching rate, high object trajectory false alarm rate, etc., this paper proposes a multi-object tracking algorithm based on pedestrian re-identification network and CNN-GRU (Convolutional Neural Networks-Gated Recurrent Unit) metric network. By constructing a deep metric model combining CNN and dual GRU network, the time characteristics of both the appearance and the motion features of the tracking object trajectory boxes can be predicted simultaneously so that the extracted objects have more discriminative features and the ID switch rate of object can be reduced. The CNN-GRU network is utilized to automatically learn the correct matching probability of historical object trajectory and different attentions are assigned to different track trajectory boxes of the same object. Thus, the proposed algorithm can effectively attenuate the influence of mis-detected object boxes in the object trajectory on the overall features of the object, meanwhile, aggregate the features of the object trajectory box. Moreover, it combines the similarity of the detection boxes and the calculated trajectory boxes via the features of pedestrian re-identification network, and takes the similarity CNN-GRU network output as the matching cost of data association part. Finally, the experimental evaluation results on a standard multi-object tracking dataset verify the effectiveness of the proposed algorithm.