CBRA: Color-based ranking aggregation for person re-identification

The problem of automatically tracking a pedestrian within camera networks with non-overlapping field-of-view, known as person re-identification, is a challenging task with still suboptimal results. Different features have been proposed in the literature, specially colors which achieved the best results when fused in a unique feature representation. Despite being better than considering individually, the fusion still does not explores all the feature discriminative power. Therefore, we propose the use of rank aggregation to improve the results. In this paper, we address the person re-identification problem using a Color-based Ranking Aggregation (CBRA) method, which explores different feature representations to obtain complementary ranking lists and combine them using the Stuart ranking aggregation method. The obtained experimental results demonstrate a great improvement in state-of-the-art, reaching top-1 rank recognition rates of 50.0% and 56.9% in the ViPER and PRTD450S data sets, respectively.