Person Retrieval in Physical World

Person re-identification (re-ID) gains plenty of achievements as a retrieval problem in constrained camera networks. However, most of the researches are concentrated on visual appearance, they still suffer from the complicated environments in unconstrained urban/campus surveillance scenario due to unreliable visual representations with extremely challenging problems as lack of training samples, amounts of irrelevant crowds, etc. Besides, most of the existing person re-ID datasets neglect the physical truth of realistic investigation application: 1) investigators search only few suspects among amounts of crowds. Moreover, he may not go through by every camera in the surveillance area and may appear in the same camera several times; and 2) the corresponding characteristic in multi-space of the same ID can be verified with each other. Therefore, we propose a person retrieval in physical world (PRPW) dataset with large-scale unconstrained surveillance scenario. It contains over 1.4 million bounding boxes, including 20 labeled IDs and numerous irrelevant crowds captured by 86 cameras. Furthermore, over 30,000 records of 20 mobile trajectories are collected in this dataset, and the 20 mobile trajectories are partially overlapped while passing by 86 cameras. Finally, based on two common senses and a verification experiment, we provide a proposal to tackle with PRPW task on the basis of trajectory association which utilizes global optimization to compensate for the errors caused by visual expression on local observation points. The comparison experiments with two typical unsupervised person re-ID methods are implemented on the constructed dataset.