Robust human appearance matching across multi-cameras

In this paper, we present a novel solution to the problem of human appearance matching across multiple cameras. Humans are represented by a set of feature points sampled from upper bodies. The problem of appearance matching across multiple cameras is formulated as finding corresponding points in two upper bodies from different views based on dissimilarity of region signatures as well as geometric constraints between feature points. For dissimilarity of region signatures, we first use k-means clustering to describe the region around the feature point, then estimate the dissimilarity between different regions under integer optimization framework. For geometric constraints, we get the spatial information of feature points based on a scale and rotation invariant constraint method. Lastly, agglomerative clustering algorithm is used to find the correct cluster of candidate pairs. Our method is robust to both outliers and deformation, and the experimental results show promising matching results on multiple cameras.