Abstract
Recognizing the same person across different camera views is crucial and yet difficult task in video surveillance. The difficulty lies in finding the matched image-pair against drastic variations in appearances and structures of the individuals. In this article, we present a three stage person re-identification framework to establish the correspondence among persons observed across non-overlapping camera views. In first stage, we propose to apply an algorithm for handling the illumination variations in image pairs. A pyramidal body partitioning scheme is then introduced to handle the viewpoint variations, in which it segments the pedestrian image into several logical parts. In second stage, we formulate an ensemble weighted hypergraph partitioning strategy that divides the gallery candidates into a set of groups with high intra-group and low inter-group commonality. A weighing scheme is suggested to find the contribution of each feature channel towards defining a group. Furthermore, we generate a set of inlier groups for each probe, where the probability of finding the desired match pair is high. In final stage, contributory weights are fused with the correlation-based similarity measure to find the corresponding match within the inlier group. Extensive experiments are carried out on three challenging datasets to evaluate the effectiveness of our proposed framework. The experimental results demonstrate that the proposed framework can achieve better performance compared with the existing methods.
Published Version
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