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 is challenging in crowd and non-overlapping camera networks. ► Motion models are used for re-identification. ► Top-part of a person is the most representative in crowded scenarios. ► Combination of motion and appearance models improve re-identification.