101. DukeMTMC4ReID: A Large-Scale Multi-camera Person Re-identification Dataset
- Author
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Wenqian Liu, Mengran Gou, Srikrishna Karanam, Richard J. Radke, and Octavia Camps
- Subjects
Computer science ,business.industry ,Feature extraction ,020207 software engineering ,02 engineering and technology ,Disjoint sets ,Multi camera ,computer.software_genre ,Machine learning ,Re identification ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,Data mining ,business ,Scale (map) ,computer - Abstract
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[36]. 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. more...
- Published
- 2017
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