Transfer re-identification: From person to set-based verification
Solving the person re-identification problem has become important for understanding people's behaviours in a multicamera network of non-overlapping views. In this work, we address the problem of re-identification from a set-based verification perspective. More specifically, we have a small set of target people on a watch list (a set) and we aim to verify whether a query image of a person is on this watch list. This differs from the existing person re-identification problem in that the probe is verified against a small set of known people but requires much higher degree of verification accuracy with very limited sampling data for each candidate in the set. That is, rather than recognising everybody in the scene, we consider identifying a small set of target people against non-target people when there is only a limited number of target training samples and a large number of unlabelled (unknown) non-target samples available. To this end, we formulate a transfer learning framework for mining discriminant information from non-target people data to solve the watch list set verification problem. Based on the proposed approach, we introduce the concepts of multi-shot and one-shot verifications. We also design new criteria for evaluating the performance of the proposed transfer learning method against the i-LIDS and ETHZ data sets.