An End-to-End Noise-Weakened Person Re-Identification and Tracking With Adaptive Partial Information.

Aiming to recognize persons of interest cross cameras in different locations, the technique of person re-identification (re-ID) has attracted unprecedented attention in the field of public security. However, most of the existing work ignores the influence of background noise and pedestrian's partial information on recognition accuracy. Moreover, the tracking procedure which has a great importance on the real world is often stripped out of the re-ID framework. Therefore, this paper proposes an end-to-end noise-weakened person re-ID and tracking model with adaptive partial information. First, to suppress the background noise and improve the feature discriminability, Mask R-CNN is applied to extract the foreground "pedestrians" out of the complicated background for feature supplement. Second, an adaptive pose estimation model is proposed to make an in-depth analysis of every human body part, thus boosting the robustness against the posture change and individual difference. Finally, to fuse the tracking procedure, a scope prediction scheme based on the pedestrian's moving speed is presented to replace the traditional full domain estimation approach, thus greatly reducing the computational complexity. The extensive experiments have been conducted and the results demonstrate that our method achieves 89.78% and 81.87% rank 1 accuracy on Market-1501 and DukeMTMC-reID with real-time tracking capability, which exhibits great superiority than the state-of-the-art methods.