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T.: The re-identification challenge (2014)
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| Venue: | In: Person Re-Identification |
| Citations: | 6 - 5 self |
BibTeX
@INPROCEEDINGS{Gong14t.:the,
author = {Shaogang Gong and Marco Cristani and Chen Change Loy and Timothy M. Hospedales and Shaogang Gong and Marco Cristani and Chen Change Loy and Timothy M. Hospedales and Shaogang Gong and Marco Cristani and Chen Change Loy and Timothy M. Hospedales},
title = {T.: The re-identification challenge},
booktitle = {In: Person Re-Identification},
year = {2014},
pages = {1--20},
publisher = {Springer}
}
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Abstract
Abstract For making sense of the vast quantity of visual data generated by the rapid expansion of large scale distributed multi-camera systems, automated per-son re-identification is essential. However, it poses a significant challenge to com-puter vision systems. Fundamentally, person re-identification requires to solve two difficult problems of ‘finding needles in haystacks ’ and ‘connecting the dots ’ by identifying instances and associating the whereabouts of targeted people travelling across large distributed space-time locations in often crowded environments. This capability would enable the discovery of, and reasoning about, individual-specific long-term structured activities and behaviours. Solving the person re-identification problem is inherently challenging but promises enormous potential for a wide range of practical applications, ranging from security and surveillance to retail and health-care. As a result, the field has drawn growing and wide interest from academic researchers and industrial developers. This chapter introduces the re-identification problem, highlights the difficulties in building person re-identification systems, and presents an overview of recent progress and the state-of-the-art approaches to solv-ing some of the fundamental challenges in person re-identification, benefiting from research in computer vision, pattern recognition and machine learning, and drawing insights from video analytics system design considerations for engineering practical solutions. It also provides an introduction to the contributing chapters of this book. The chapter ends by posing some open questions for the re-identification challenge arising from emerging and future applications.
Keyphrases
re-identification challenge person re-identification wide interest academic researcher multi-camera system state-of-the-art approach open question wide range future application significant challenge crowded environment engineering practical solution rapid expansion computer vision per-son re-identification space-time location difficult problem fundamental challenge practical application re-identification problem person re-identification problem vast quantity com-puter vision system visual data promise enormous potential pattern recognition video analytics system design consideration recent progress large scale person re-identification system targeted people machine learning individual-specific long-term structured activity industrial developer
