@inproceedings{83ea1f001de240ebbcb135a358f14efa,
title = "Towards Person Identification and Re-identification with Attributes",
abstract = "Visual identification of an individual in a crowded environment observed by a distributed camera network is critical to a variety of tasks including commercial space management, border control, and crime prevention. Automatic re-identification of a human from public space CCTV video is challenging due to spatiotemporal visual feature variations and strong visual similarity in people{\textquoteright}s appearance, compounded by low-resolution and poor quality video data. Relying on re-identification using a probe image is limiting, as a linguistic description of an individual{\textquoteright}s profile may often be the only available cues. In this work, we show how mid-level semantic attributes can be used synergistically with low-level features for both identification and re-identification. Specifically, we learn an attribute-centric representation to describe people, and a metric for comparing attribute profiles to disambiguate individuals. This differs from existing approaches to re-identification which rely purely on bottom-up statistics of low-level features: it allows improved robustness to view and lighting; and can be used for identification as well as re-identification. Experiments demonstrate the flexibility and effectiveness of our approach compared to existing feature representations when applied to benchmark datasets.",
author = "Ryan Layne and Hospedales, {Timothy M.} and Shaogang Gong",
year = "2012",
doi = "10.1007/978-3-642-33863-2_40",
language = "English",
isbn = "978-3-642-33862-5",
series = "Lecture Notes in Computer Science (LNCS)",
publisher = "Springer",
pages = "402--412",
booktitle = "Computer Vision - ECCV 2012. Workshops and Demonstrations - Florence, Italy, October 7-13, 2012, Proceedings, Part I",
address = "United Kingdom",
}