Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
Abstract
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 1080 p, 60 fps video taken by 8 cameras observing more than 2,700 identities over 85 min; 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.
Cite
Text
Ristani et al. "Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-48881-3_2Markdown
[Ristani et al. "Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/ristani2016eccv-performance/) doi:10.1007/978-3-319-48881-3_2BibTeX
@inproceedings{ristani2016eccv-performance,
title = {{Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking}},
author = {Ristani, Ergys and Solera, Francesco and Zou, Roger S. and Cucchiara, Rita and Tomasi, Carlo},
booktitle = {European Conference on Computer Vision},
year = {2016},
pages = {17-35},
doi = {10.1007/978-3-319-48881-3_2},
url = {https://mlanthology.org/eccv/2016/ristani2016eccv-performance/}
}