Features for Multi-target Multi-camera Tracking and Re-identification

Published

Conference Paper

© 2018 IEEE. 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 available1.

Full Text

Duke Authors

Cited Authors

  • Ristani, E; Tomasi, C

Published Date

  • December 14, 2018

Published In

Start / End Page

  • 6036 - 6046

International Standard Serial Number (ISSN)

  • 1063-6919

International Standard Book Number 13 (ISBN-13)

  • 9781538664209

Digital Object Identifier (DOI)

  • 10.1109/CVPR.2018.00632

Citation Source

  • Scopus