Awesome

Person Re-identification Benchmark

This repository hosts the codebase for the following work: Karanam, S., Gou, M., Wu, Z., Rates-Borras, A., Camps, O., & Radke, R. J. (2018). A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets. IEEE Transactions on Pattern Analysis and Machine Intelligence, accepted February 2018.

Tested on Windows Server 2012 with MATLAB 2016b

Quick Start

• Clone this repository
• Run a quick example in run_experiment_benchmark.m
• Read the results for VIPeR dataset with WHOS feature and XQDA

Run other experiments

• Download supported dataset, unzip it and put it under the folder ./Data
• Download corresponding partition file and put it under the folder ./TrainTestSplits
• Run corresponding prepare_DATANAME.m inside the folder ./Data (if avaliable)
• Change the parameters in run_experiment_benchmark.m

Check List for supported/tested feature

• HistLBP
• WHOS
• gBiCov
• LDFV
• ColorTexture\ELF
• LOMO (Windows)
• GOG (Windows)

Check List for supported/tested metric learning

• FDA
• LFDA
• kLFDA-linear/chi2/chi2-rbf/exp
• XQDA
• MFA
• kMFA-linear/chi2/chi2-rbf/exp
• NFST
• KISSME
• PCCA-linear/chi2/chi2-rbf/exp
• rPCCA-linear/chi2/chi2-rbf/exp
• kPCCA-linear/chi2/chi2-rbf/exp
• PRDC
• SVMML
• kCCA

Check List for supported/tested multi-shot ranking method

• rnp
• srid
• ahisd

Check List for supported/tested dataset

• VIPeR Parition included in repo
• Airport Partition comes with dataset
• DukeMTMC4ReID Partition comes with dataset
• Market1501 Partition
• CAVIAR (WHOS feature only) Parition included in repo

Reference

Please cite the work appropriately for each used feature/metric learning/ranking/dataset

@ARTICLE{8294254, 
author={S. Karanam and M. Gou and Z. Wu and A. Rates-Borras and O. Camps and R. J. Radke}, 
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
title={A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets}, 
year={2018}, 
keywords={Benchmark testing;Cameras;Feature extraction;Histograms;Image color analysis;Measurement;Probes}, 
doi={10.1109/TPAMI.2018.2807450}, 
ISSN={0162-8828}, 
}