A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets

TL;DR

This paper provides a comprehensive benchmark for person re-identification, evaluating various features and metrics across multiple datasets to facilitate fair comparison and advance research in the field.

Contribution

It introduces a unified evaluation framework with a large-scale dataset and implements a wide range of algorithms for consistent comparison.

Findings

Unified code library for fair comparison

Evaluation on a new large-scale dataset

Benchmark results across 16 public datasets

Abstract

Person re-identification (re-id) is a critical problem in video analytics applications such as security and surveillance. The public release of several datasets and code for vision algorithms has facilitated rapid progress in this area over the last few years. However, directly comparing re-id algorithms reported in the literature has become difficult since a wide variety of features, experimental protocols, and evaluation metrics are employed. In order to address this need, we present an extensive review and performance evaluation of single- and multi-shot re-id algorithms. The experimental protocol incorporates the most recent advances in both feature extraction and metric learning. To ensure a fair comparison, all of the approaches were implemented using a unified code library that includes 11 feature extraction algorithms and 22 metric learning and ranking techniques. All approaches were evaluated using a new large-scale dataset that closely mimics a real-world problem setting, in addition to 16 other publicly available datasets: VIPeR, GRID, CAVIAR, DukeMTMC4ReID, 3DPeS, PRID, V47, WARD, SAIVT-SoftBio, CUHK01, CHUK02, CUHK03, RAiD, iLIDSVID, HDA+ and Market1501. The evaluation codebase and results will be made publicly available for community use.