Human Re-identification by Matching Compositional Template with Cluster Sampling

TL;DR

This paper introduces a novel human re-identification method that constructs compositional templates from reference images and employs a cluster sampling algorithm within a graph framework to improve matching accuracy under pose, view, and occlusion variations.

Contribution

It proposes a new compositional template construction and a cluster sampling matching algorithm for robust human re-identification in surveillance scenarios.

Findings

Outperforms existing methods on three public datasets.

Effectively handles pose, view, and occlusion variations.

Demonstrates superior matching accuracy and robustness.

Abstract

This paper aims at a newly raising task in visual surveillance: re-identifying people at a distance by matching body information, given several reference examples. Most of existing works solve this task by matching a reference template with the target individual, but often suffer from large human appearance variability (e.g. different poses/views, illumination) and high false positives in matching caused by conjunctions, occlusions or surrounding clutters. Addressing these problems, we construct a simple yet expressive template from a few reference images of a certain individual, which represents the body as an articulated assembly of compositional and alternative parts, and propose an effective matching algorithm with cluster sampling. This algorithm is designed within a candidacy graph whose vertices are matching candidates (i.e. a pair of source and target body parts), and iterates in two steps for convergence. (i) It generates possible partial matches based on compatible and competitive relations among body parts. (ii) It confirms the partial matches to generate a new matching solution, which is accepted by the Markov Chain Monte Carlo (MCMC) mechanism. In the experiments, we demonstrate the superior performance of our approach on three public databases compared to existing methods.