Pedestrian Re-identification by Graph Clustering

Matching people in multi-camera views, known as pedestrian re-identification problem, is a challenge task. Searching a designated pedestrian at the entire monitoring scene has been achieved in previous work. However, the existing results only return a sequence of pedestrian images ranked by the similarity with the input image, rather than matched images. In this paper, we use graph partitioning methods to solve the pedestrian re-identification problem. We first get a similarity matrix by calculating the similarity between each image and others. Then we consider the matrix as an undirected graph and use graph partitioning methods to partition it. The result of graph partitioning corresponds to the classification of pedestrian images. The main contributions of this paper include 1) we estimate the number of pedestrians on the multi-camera views, 2) we label a same object ID for sample images of the same pedestrian.