Abstract
Re-identification systems aim at recognizing the same individuals in multiple cameras, and one of the most relevant problems is that the appearance of same individual varies across cameras due to illumination and viewpoint changes. This paper proposes the use of cumulative weighted brightness transfer functions (CWBTFs) to model these appearance variations. Different from recently proposed methods which only consider pairs of images to learn a brightness transfer function, we exploit such a multiple-frame-based learning approach that leverages consecutive detections of each individual to transfer the appearance. We first present a CWBTF framework for the task of transforming appearance from one camera to another. We then present a re-identification framework where we segment the pedestrian images into meaningful parts and extract features from such parts, as well as from the whole body. Jointly, both of these frameworks contribute to model the appearance variations more robustly. We tested our approach on standard multi-camera surveillance datasets, showing consistent and significant improvements over existing methods on three different datasets without any other additional cost. Our approach is general and can be applied to any appearance-based method.
Highlights
Person re-identification (ReID) refers to the problem of recognizing individuals at different times and locations
We compare our approach with several alternatives, which fall into two categories: (i) direct methods including symmetry-driven accumulation of local features (SDALF) [10], Custom Pictorial Structures (CPS) [11] and stel component analysis (SCA) [12]; (ii) transform learning-based methods including Implicit Camera Transfer (ICT) [35], Explicit Camera Transfer (ECT) [35], Mean Brightness Transfer (MBTF) [1],Cumulative Brightness Transfer Function (CBTF) [3], Weighted Brightness Transfer Function (WBTF) [4], cumulative weighted brightness transfer functions (CWBTFs) with CPS [50], and warp function space (WFS) [37]
The gap is still significant compared to CWBTF; (iii) the improvement over CBTF shows that the proposed brightness transfer function is very effective in transferring brightness across the camera, irrespective of the feature representation
Summary
Person re-identification (ReID) refers to the problem of recognizing individuals at different times and locations. The core assumption in re-identification is that individuals do not change their clothing, so that appearances in the several views are similar; it still consists of a very challenging task due to the non-rigid structure of the human body, the different perspectives with which a pedestrian can be observed, and the highly variable illumination conditions. Illumination variations between non-overlapping camera views can have an immense effect on the appearance of an individual, increasing the difficulty of associating people in person re-identification. The variations of illumination can be observed in the same individual at different instants, even for the same camera
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
