Features for Multi-Target Multi-Camera Tracking and Re-Identification

Ergys Ristani, Carlo Tomasi; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 6036-6046

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.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Ristani_2018_CVPR,
author = {Ristani, Ergys and Tomasi, Carlo},
title = {Features for Multi-Target Multi-Camera Tracking and Re-Identification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}