Multi-Shot Re-Identification with Random-Projection-Based Random Forests

Abstract

Human re-identification remains one of the fundamental, difficult problems in video surveillance and analysis. Current metric learning algorithms mainly focus on finding an optimized vector space such that observations of the same person in this space have a smaller distance than observations of two different people. In this paper, we propose a novel metric learning approach to the human reidentification problem, with an emphasis on the multi-shot scenario. First, we perform dimensionality reduction on image feature vectors through random projection. Next, a random forest is trained based on pair wise constraints in the projected subspace. This procedure repeats with a number of random projection bases, so that a series of random forests are trained in various feature subspaces. Finally, we select personalized random forests for each subject using their multi-shot appearances. We evaluate the performance of our algorithm on three benchmark datasets.