Images-to-images person ReID without temporal linking Online publication date: Tue, 02-Apr-2019
by Thuy-Binh Nguyen; Thi-Lan Le; Ngoc-Nam Pham
International Journal of Computational Vision and Robotics (IJCVR), Vol. 9, No. 2, 2019
Abstract: This paper addresses images-to-images person re-identification (ReID) in which there are multiple images for each individual on both gallery and probe. Most existing approaches that try to extract/learn features require temporal linking between frames. This paper proposes a novel framework to overcome this requirement by formulating images-to-images person re-identification as a fusion function of image-to-images. First, a ranked list of candidates corresponding to each query image is determined. Then, these lists are fused to determine the matched person. The contributions of the paper are two-fold: 1) an extra feature [Gaussian of Gaussian (GOG)] is used for representing person; 2) new images-to images scheme that does not require temporal linking and features the benefit of image-to-images scheme is proposed. Extensive experiments on CAVIAR4REID (cases A and B) and RAiD datasets prove the effectiveness of the framework. The proposed scheme obtains +20.88%, +10.23%, and +10.39% improvement in rank-1 over image-to-images scheme on these datasets.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Vision and Robotics (IJCVR):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook