A Multiple Component Matching Framework for Person Re-Identification

TL;DR

This paper introduces a novel Multiple Component Matching framework for person re-identification, unifying existing techniques and proposing a new robust method that achieves state-of-the-art results under varying lighting conditions.

Contribution

It presents a new framework inspired by Multiple Component Learning, unifying prior methods and introducing a simple, robust re-identification technique with superior performance.

Findings

Achieved state-of-the-art performance in person re-identification.

Demonstrated robustness to lighting variations.

Unified existing methods under a new framework.

Abstract

Person re-identification consists in recognizing an individual that has already been observed over a network of cameras. It is a novel and challenging research topic in computer vision, for which no reference framework exists yet. Despite this, previous works share similar representations of human body based on part decomposition and the implicit concept of multiple instances. Building on these similarities, we propose a Multiple Component Matching (MCM) framework for the person re-identification problem, which is inspired by Multiple Component Learning, a framework recently proposed for object detection. We show that previous techniques for person re-identification can be considered particular implementations of our MCM framework. We then present a novel person re-identification technique as a direct, simple implementation of our framework, focused in particular on robustness to varying lighting conditions, and show that it can attain state of the art performances.