Learning to rank in person re-identification with metric ensembles

TL;DR

This paper introduces a structured learning approach for person re-identification that combines multiple features and optimizes evaluation metrics, significantly improving recognition rates on several benchmarks.

Contribution

It presents a novel metric ensemble framework with optimization algorithms directly targeting re-identification evaluation measures, outperforming existing methods.

Findings

Achieved state-of-the-art rank-1 recognition rates on multiple benchmarks.

Improved recognition rates significantly compared to previous methods.

Demonstrated practical effectiveness for real-world surveillance applications.

Abstract

We propose an effective structured learning based approach to the problem of person re-identification which outperforms the current state-of-the-art on most benchmark data sets evaluated. Our framework is built on the basis of multiple low-level hand-crafted and high-level visual features. We then formulate two optimization algorithms, which directly optimize evaluation measures commonly used in person re-identification, also known as the Cumulative Matching Characteristic (CMC) curve. Our new approach is practical to many real-world surveillance applications as the re-identification performance can be concentrated in the range of most practical importance. The combination of these factors leads to a person re-identification system which outperforms most existing algorithms. More importantly, we advance state-of-the-art results on person re-identification by improving the rank-$1$ recognition rates from $40\%$ to $50\%$ on the iLIDS benchmark, $16\%$ to $18\%$ on the PRID2011 benchmark, $43\%$ to $46\%$ on the VIPeR benchmark, $34\%$ to $53\%$ on the CUHK01 benchmark and $21\%$ to $62\%$ on the CUHK03 benchmark.