LAUSR

Person reidentification (re-id), an emerging problem in visual surveillance, deals with maintaining the identities of individuals while they traverse various locations surveilled by a camera network. Motivated by real-world scenarios, we propose a method that seeks to simultaneously identify who among a group of individuals viewed in one view are present/absent in the other. From a visual perspective, re-id is challenging due to significant changes in visual appearance of individuals in cameras with different pose, illumination, and calibration. Globally, the challenge arises from the need to maintain structurally consistent matches among all the individual entities across different camera views. We propose person re-id via structured matching (PRISM), an SM method to jointly account for these challenges. We view the global problem as a weighted graph matching problem and estimate edge weights by learning to predict them based on the co-occurrences of visual patterns in the training examples. These co-occurrence-based scores in turn account for appearance changes by inferring likely and unlikely visual co-occurrences appearing in training instances. We implement PRISM on single-shot and multishot scenarios. PRISM uniformly outperforms state of the art in terms of matching rate while being robust and computationally efficient.