DukeMTMC4ReID: A Large-Scale Multi-Camera Person Re-Identification Dataset

Mengran Gou, Srikrishna Karanam, Wenqian Liu, Octavia Camps, Richard J. Radke; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 10-19


In the past decade, research in person re-identification (re-id) has exploded due to its broad use in security and surveillance applications. Issues such as inter-camera viewpoint, illumination and pose variations make it an extremely difficult problem. Consequently, many algorithms have been proposed to tackle these issues. To validate the efficacy of re-id algorithms, numerous benchmarking datasets have been constructed. While early datasets contained relatively few identities and images, several large-scale datasets have recently been proposed, motivated by data-driven machine learning. In this paper, we introduce a new large-scale real-world re-id dataset, DukeMTMC4ReID, using 8 disjoint surveillance camera views covering parts of the Duke University campus. The dataset was created from the recently proposed fully annotated multi-target multi-camera tracking dataset DukeMTMC. A benchmark summarizing extensive experiments with many combinations of existing re-id algorithms on this dataset is also provided for an up-to-date performance analysis.

Related Material

author = {Gou, Mengran and Karanam, Srikrishna and Liu, Wenqian and Camps, Octavia and Radke, Richard J.},
title = {DukeMTMC4ReID: A Large-Scale Multi-Camera Person Re-Identification Dataset},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}