Online inter-camera trajectory association exploiting person re-identification and camera topology

Online inter-camera trajectory association is a promising topic in intelligent video surveillance, which concentrates on associating trajectories belong to the same individual across different cameras according to time. It remains challenging due to the inconsistent appearance of a person in different cameras and the lack of spatio-temporal constraints between cameras. Besides, the orientation variations and the partial occlusions significantly increase the difficulty of inter-camera trajectory association. Targeting to solve these problems, this work proposes an orientation-driven person reidentification (ODPR) and an effective camera topology estimation based on appearance features for online inter-camera trajectory association. ODPR explicitly leverages the orientation cues and stable torso features to learn discriminative feature representations for identifying trajectories across cameras, which alleviates the pedestrian orientation variations by the designed orientation-driven loss function and orientation aware weights. The effective camera topology estimation introduces appearance features to generate the correct spatio-temporal constraints for narrowing the retrieval range, which improves the time efficiency and provides the possibility for intelligent inter-camera trajectory association in large-scale surveillance environments. Extensive experimental results demonstrate that our proposed approach significantly outperforms most state-of-the-art methods on the popular person re-identification datasets and the public multi-target, multi-camera tracking benchmark.