Learning Appearance Transfer for Person Re-identification

In this chapter we review methods that model the transfer a person’s appearance undergoes when passing between two cameras with non-overlapping fields of view. Whereas many recent studies deal with re-identifying a person at any new location and search for universal signatures and metrics, here we focus on solutions for the natural setup of surveillance systems in which the cameras are specific and stationary, solutions which exploit the limited transfer domain associated with a specific camera pair. We compare the performance of explicit transfer modeling, implicit transfer modeling, and camera-invariant methods. Although explicit transfer modeling is advantageous over implicit transfer modeling when the inter-camera training data is poor, implicit camera transfer, which can model multi-valued mappings and better utilizes negative training data, is advantageous when a larger training set is available. While camera-invariant methods have the advantage of not relying on specific inter-camera training data, they are outperformed by both cameratransfer approaches when sufficient training data is available. We therefore conclude that camera-specific information is very informative for improving re-identification in sites with static non-overlapping cameras and that it should still be considered even with the improvement of camera-invariant methods.