Person Re-identification Based on Global Color Context
In this paper, we present a new solution to the problem of person re-identification. Person re-identification means to match observations of the same person across different time and possibly different cameras. The appearance based person re-identification must deal with several challenges such as variations of illumination conditions, poses and occlusions. Our proposed method inspires from the spirit of self-similarity. Self-similarity is an attractive property in visual recognition. Instead of comparing image descriptors between two images directly, the self-similarity measures how similar they are to a neighborhood of themselves. The self-similarities of image patterns within the image are modeled in two different ways in the proposed Global Color Context (GCC) method. The spatial distributions of self-similarities w.r.t. color words are combined to characterize the appearance of pedestrians. Promising results are obtained in the public ETHZ database compared with state-of-art performances.
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