Person re-identification in crowd
Person re-identification aims to recognize the same person viewed by disjoint cameras at different time instants and locations. In this paper, after an extensive review of state-of-the-art approaches, we propose a re-identification method that takes into account the appearance of people, the spatial location of cameras and potential paths a person can choose to follow. This choice is modeled with a set of areas of interest (landmarks) that constrain the propagation of people trajectories in non-observed regions between the field-of-view of cameras. We represent people with a selective patch around their upper body to work in crowded scenes when occlusions are frequent. We demonstrate the proposed method in a challenging scenario from London Gatwick airport and compare it to well-known person re-identification methods, highlighting their strengths and limitations. Finally, we show by Cumulative Matching Characteristic curve that the best performance results by modeling people movements in non-observed regions combined with appearance methods, achieving an average improvement of 6% when only appearance is used and 15% when only motion is used for the association of people across cameras.
person re-identification non-overlapping cameras people in crowd trajectory propagation appearance features london gatwick airport dataset