Optimizing Mean Reciprocal Rank for person re-identification

Person re-identification is one of the most challenging issues in network-based surveillance. The difficulties mainly come from the great appearance variations induced by illumination, camera view and body pose changes. Maybe influenced by the research on face recognition and general object recognition, this problem is habitually treated as a verification or classification problem, and much effort has been put on optimizing standard recognition criteria. However, we found that in practical applications the users usually have different expectations. For example, in a real surveillance system, we may expect that a visual user interface can show us the relevant images in the first few (e.g. 20) candidates, but not necessarily before all the irrelevant ones. In other words, there is no problem to leave the final judgement to the users. Based on such an observation, this paper treats the re-identification problem as a ranking problem and directly optimizes a listwise ranking function named Mean Reciprocal Rank (MRR), which is considered by us to be able to generate results closest to human expectations. Using a maximum-margin based structured learning model, we are able to show improved re-identification results on widely-used benchmark datasets.