Online pedestrian tracking with multi-stage re-identification

Nowadays the task of tracking pedestrians is often addressed within a tracking-by-detection framework, which in most cases entails that the position of each target has been detected before tracking begins. However in some cases, a pedestrian who is being tracked may be obscured by other targets or obstacles, and during this period they may change their trajectory or speed (track drift), and sometimes such a target may leave the FOV (Field of View) [10] but appear again later. These temporary disappearances and absence of detections disrupt the work of the detectors to such an extent that there is a significant decline in performance. In this paper, we propose a novel approach to pedestrian tracking based on multi-stage re-identification. To deal with the problems discussed above, the proposed framework is comprised of a two-stage re-identification algorithm dealing with cases of track drift and re-entry into the FOV individually, in order to match the identities of lost and reappeared targets through a comparison of the affinities between their appearance, size and position, and also to update the status of re-identified targets through this assessment. The experimental results demonstrate that this framework can effectively handle complex temporary lost and re-entry situations with robustness, and that its performance is state of the art.