To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error; (ii) the largest fully-annotated and calibrated data set to date with more than 2 million frames of 1080p, 60fps video taken by 8 cameras observing more than 2,700 identities over 85 minutes; and (iii) a reference software system as a comparison baseline. We show that (i) our measures properly account for bottom-line identity match performance in the multi-camera setting; (ii) our data set poses realistic challenges to current trackers; and (iii) the performance of our system is comparable to the state of the art.
@article{arxiv.1609.01775,
title = {Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking},
author = {Ergys Ristani and Francesco Solera and Roger S. Zou and Rita Cucchiara and Carlo Tomasi},
journal= {arXiv preprint arXiv:1609.01775},
year = {2016}
}
Comments
ECCV 2016 Workshop on Benchmarking Multi-Target Tracking