@inproceedings{929cf4e0b6ab43ab9474cc5bbfed7c5f,
title = "POI: Multiple object tracking with high performance detection and appearance feature",
abstract = "Detection and learning based appearance feature play the central role in data association based multiple object tracking (MOT), but most recent MOT works usually ignore them and only focus on the hand-crafted feature and association algorithms. In this paper, we explore the high-performance detection and deep learning based appearance feature, and show that they lead to significantly better MOT results in both online and offline setting. We make our detection and appearance feature publicly available (https://drive.google.com/open? id=0B5ACiy41McAHMjczS2p0dFg3emM). In the following part, we first summarize the detection and appearance feature, and then introduce our tracker named Person of Interest (POI), which has both online and offline version (We use POI to denote our online tracker and KDNT to denote our offline tracker in submission.).",
author = "Fengwei Yu and Wenbo Li and Quanquan Li and Yu Liu and Xiaohua Shi and Junjie Yan",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; Computer Vision - ECCV 2016 Workshops, Proceedings ; Conference date: 08-10-2016 Through 16-10-2016",
year = "2016",
doi = "10.1007/978-3-319-48881-3\_3",
language = "英语",
isbn = "9783319488806",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "36--42",
editor = "Gang Hua and Herve Jegou",
booktitle = "Computer Vision – ECCV 2016 Workshops, Proceedings",
address = "德国",
}