NDLI: Video-based person re-identification based on regularised hull distance learning

Content Provider	IET Digital Library
Author	Xu, Xiaoyue Chen, Ying
Abstract	Existing person re-identification (re-id) models mainly focus on still-image-based module, namely matching person images across non-overlapping camera views. Since video sequence contains much more information than still images and can be easily achieved by tracking algorithms in practical applications, the video re-id has attracted increasing attention in recent years. Distance learning is crucial for a re-id system. However, the computed distances in traditional video-based methods are easily distracted by the randomness of data distribution, especially with small sample size for training. To preferably distinguish different people, a novel regularised hull distance learning video-based person re-id method is proposed. It is advantageous in two aspects: robustness is guaranteed due to expanded video samples by regularised affine hull with limited ones, discriminability is ensured due to penalised hard negative samples more severely. Hence, the discriminability and robustness of the learnt metric are strengthened. Comparisons with the state-of-the-art video-based methods as well as related methods on PRID 2011, iLIDS-VID and MARS datasets demonstrate the superiority of the authors’ method.
Starting Page	385
Ending Page	394
Page Count	10
ISSN	17519632
Volume Number	13
e-ISSN	17519640
Issue Number	Issue 4, Jun (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/13/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5130
Journal	IET Computer Vision
Publisher Date	2018-11-13
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Camera Computed Distances Computer Vision And Image Processing Technique Computer-aided Instruction Distance Learning Expanded Video Sample Image Matching Image Recognition Image Sequence Image Sonsor Knowledge Engineering Technique Learning in AI Nonoverlapping Camera Views Optical, Image And Video Signal Processing Penalised Hard Negative Sample Person Image Re-id System Regularised Affine Hull Regularised Hull Distance Learning Sample Size State-of-the-art Video-based Method Still-image-based Module Traditional Video-based Method Video Re-id Video Sequence Video Signal Processing Video-based Person Re-id Method Video-based Person Re-identification
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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