NDLI: Pedestrian re-identification via coarse-to-fine ranking

Content Provider	IET Digital Library
Author	Xiaokai, Liu
Abstract	Appearance-based person re-identification is particularly difficult due to varying lighting conditions and pose variations across camera views. Taking inspiration from image retrieval, in which windowed searching over locations is proven to be more effective, the authors first perform dense local feature matching using graph cuts to properly deal with the pose variation problem. However, the re-identification problem suffers from far more overlap between feature distributions. In a re-identification problem, many samples cropped from surveillance videos are heavily contaminated by external factors or internal mechanical noises, making the images from the same pedestrian totally different. These overly difficult samples would significantly degenerate the training performance. To address this problem, a query-level loss function for ranking is proposed, benefiting from taking into account the training data every query set to decrease the punishment for those morbid samples. The authors further develop a coarse-to-fine iterative algorithm, where the update in each iteration is computed by solving a gradient-based optimisation and update iteration is to refine the training data by adjusting an ‘Expected rank’ parameter. The authors present experiments to demonstrate the performance gain of the proposed method over existing template matching and ranking models.
Starting Page	368
Ending Page	375
Page Count	8
ISSN	17519632
Volume Number	10
e-ISSN	17519640
Issue Number	Issue 5, Aug (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/10/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2014.0288
Journal	IET Computer Vision
Publisher Date	2016-03-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Appearance-based Person Re-identification Camera Camera Views Coarse-to-fine Iterative Algorithm Coarse-to-fine Ranking Computer Vision Computer Vision And Image Processing Technique Expected Rank Parameter Gradient Method Gradient-based Optimisation Image Sonsor Interpolation And Function Approximation Iterative Method Linear Algebra Mechanical Noises Numerical Analysis Optical, Image And Video Signal Processing Optimisation Optimisation Technique Pedestrian Pedestrian Re-identification Query-level Loss Function Ranking Model Re-identification Problem Reidentification Problem Surveillance Videos Template Matching Video Surveillance
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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