Design of real-world person re-identification systems

Abstract

In typical re-id research, the probe image or image sequence is matched to a fixed gallery set, ignoring the arrival time of each person. In our first contribution, we designed and collected a new, large-scale, multi-camera re-id dataset called RPIfield. It preserves time-stamp information for every detected person in each video sequence, to better simulate real-world re-id operational scenarios. With this information about candidates' reappearances, re-id algorithms can be applied to provide instantaneous rank lists for probes that simulate a real-time re-id system. Based on this dataset, we propose a new evaluation methodology called the Rank Persistence Curve (RPC) for evaluating re-id algorithms in circumstances when the same person of interest can appear multiple times in the gallery, as well as when the performance over multiple persons of interest should be aggregated. We demonstrate the effectiveness of RPCs on RPIfield for allowing users to make informed choices about the expected performance of candidate re-id algorithms in real-world deployments.