Learning to Rank in Person Re-Identification With Metric Ensembles

Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1846-1855

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.

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[bibtex]
@InProceedings{Paisitkriangkrai_2015_CVPR,
author = {Paisitkriangkrai, Sakrapee and Shen, Chunhua and van den Hengel, Anton},
title = {Learning to Rank in Person Re-Identification With Metric Ensembles},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}