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

Publication ,  Conference
Ristani, E; Tomasi, C
Published in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
December 14, 2018

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.

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Published In

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

DOI

ISSN

1063-6919

ISBN

9781538664209

Publication Date

December 14, 2018

Start / End Page

6036 / 6046
 

Citation

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Ristani, E., & Tomasi, C. (2018). Features for Multi-target Multi-camera Tracking and Re-identification. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 6036–6046). https://doi.org/10.1109/CVPR.2018.00632
Ristani, E., and C. Tomasi. “Features for Multi-target Multi-camera Tracking and Re-identification.” In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 6036–46, 2018. https://doi.org/10.1109/CVPR.2018.00632.
Ristani E, Tomasi C. Features for Multi-target Multi-camera Tracking and Re-identification. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2018. p. 6036–46.
Ristani, E., and C. Tomasi. “Features for Multi-target Multi-camera Tracking and Re-identification.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018, pp. 6036–46. Scopus, doi:10.1109/CVPR.2018.00632.
Ristani E, Tomasi C. Features for Multi-target Multi-camera Tracking and Re-identification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2018. p. 6036–6046.

Published In

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

DOI

ISSN

1063-6919

ISBN

9781538664209

Publication Date

December 14, 2018

Start / End Page

6036 / 6046