Is Re ranking Useful for Open set Person Re identification?
Re ranking algorithms can often boost the performance of close set person re identification. However, limited efforts have been devoted to answering whether a similar conclusion could be derived on open set person re identification. Considering that open set scenario is more practical in real applications, in this paper, we try to answer this question and do a benchmark study of re ranking on open set person re identification. Specifically, we evaluate three feature descriptors, namely MB LBP, LOMO, and IDE, and four distance metrics, namely Euclidean, Cosine, RRDA, and XQDA, with their combinations as baseline algorithms. Then, we evaluate four popular re ranking algorithms, including k reciprocal Encoding, ECN 3, ECN 4, and DaF. Through extensive benchmark studies on the OPeRIDv1.0 dataset, the results show that re ranking algorithms, though useful for closed set person re identification, are not generally effective for the open set person re identification problem. We argue that this is because re ranking algorithms change the score distributions per query, and hence disrupt the FAR estimation across all queries. Accordingly, we propose to align the re ranking scores to the original score via the min max normalization, which verifies our hypothesis above.
2018 IEEE International Conference on Big Data (Big Data)