Features for Multi-Target Multi-Camera Tracking and Re-Identification
Abstract
Multi-Target Multi-Camera Tracking (MTMCT) tracks many people through video taken from several cameras. Person Re-Identification (Re-ID) retrieves from a gallery images of people similar to a person query image. We learn good features for both MTMCT and Re-ID with a convolutional neural network. Our contributions include an adaptive weighted triplet loss for training and a new technique for hard-identity mining. Our method outperforms the state of the art both on the DukeMTMC benchmarks for tracking, and on the Market-1501 and DukeMTMC-ReID benchmarks for Re-ID. We examine the correlation between good Re-ID and good MTMCT scores, and perform ablation studies to elucidate the contributions of the main components of our system. Code is available.
Cite
Text
Ristani and Tomasi. "Features for Multi-Target Multi-Camera Tracking and Re-Identification." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00632Markdown
[Ristani and Tomasi. "Features for Multi-Target Multi-Camera Tracking and Re-Identification." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/ristani2018cvpr-features/) doi:10.1109/CVPR.2018.00632BibTeX
@inproceedings{ristani2018cvpr-features,
title = {{Features for Multi-Target Multi-Camera Tracking and Re-Identification}},
author = {Ristani, Ergys and Tomasi, Carlo},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2018},
doi = {10.1109/CVPR.2018.00632},
url = {https://mlanthology.org/cvpr/2018/ristani2018cvpr-features/}
}