Re-identification of Pedestrians in Crowds Using Dynamic Time Warping

This paper presents a new tracking algorithm to solve online the 'Tag and Track' problem in a crowded scene with a network of CCTV Pan, Tilt and Zoom (PTZ) cameras. The dataset is very challenging as the non-overlapping cameras exhibit pan tilt and zoom motions, both smoothly and abruptly. Therefore a tracking-by-detection approach is combined with a re-identification method based on appearance features to solve the re-acquisition problem between non overlapping camera views and crowds occlusions. However, conventional re-identification techniques of multi target trackers, which consist of learning an online appearance model to differentiate the target of interest from other people in the scene, are not suitable for this scenario because the tagged pedestrian moves in an environment where pedestrians walking with them are constantly changing. Therefore, a novel multiple shots re-identification technique is proposed which combines a standard single shot re-identification, based on offline training to recognize humans from different views, with a Dynamic Time Warping (DTW) distance.