Unsupervised data association for metric learning in the context of multi-shot person re-identification

Appearance based person re-identification is a challenging task, specially due to difficulty in capturing high intra-person appearance variance across cameras when inter-person similarity is also high. Metric learning is often used to address deficiency of low-level features by learning view specific re-identification models. The models are often acquired using a supervised algorithm. This is not practical for real-world surveillance systems because annotation effort is view dependent. In this paper, we propose a strategy to automatically generate labels for person tracks to learn similarity metric for multi-shot person re-identification task. We demonstrate on multiple challenging datasets that the proposed labeling strategy significantly improves performance of two baseline methods and the extent of improvement is comparable to that of manual annotations in the context of KISSME algorithm [14].