NDLI: Enhancing person re-identification by late fusion of low-, mid- and high-level features

Content Provider	IET Digital Library
Author	Lejbølle, Aske R. Nasrollahi, Kamal Moeslund, Thomas B.
Abstract	Person re-identification is the process of finding people across different cameras. In this process, focus often lies in developing strong feature descriptors or a robust metric learning algorithm. While the two aspects are the most important steps in order to secure a high performance, a less explored aspect is late fusion of complementary features. For this purpose, this study proposes a late fusing scheme that, based on an experimental analysis, combines three systems that focus on extracting features and provide supervised learning on different abstraction levels. To analyse the behaviour of the proposed system, both rank aggregation and score-level fusion are applied. The authors’ proposed fusion scheme increases results on both small and large datasets. Experimental results on VIPeR show accuracies 5.43% higher than related systems, while results on PRID450S and CUHK01 increase state-of-the-art results by 10.94 and 14.84%, respectively. Furthermore, a cross-dataset test shows an increased rank-1 accuracy of 28.26% when training on CUHK02 and testing on VIPeR. Finally, an analysis of the late fusion shows aggregation to be better when individual results are unequally distributed within top-10 while score-level fusion provides better results when two individual results lie within top-5 while the last lies outside top-10.
Starting Page	125
Ending Page	135
Page Count	11
ISSN	20474938
Volume Number	7
e-ISSN	20474946
Issue Number	Issue 2, Mar (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/7/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2016.0200
Journal	IET Biometrics
Publisher Date	2017-07-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Abstraction Levels Computer Vision And Image Processing Technique CUHK01 Feature Extraction High-level Feature Image Fusion Image Recognition Knowledge Engineering Technique Late Fusing Scheme Late Fusion Learning in AI Low-level Feature Mid-level Feature Person Re-identification PRID450S Rank Aggregation Robust Metric Learning Algorithm Score-level Fusion Sensor Fusion Strong Feature Descriptors Supervised Learning VIPeR
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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