People re-identification using skeleton standard posture and color descriptors from RGB-D data. (May 2019)

• Record Type: Journal Article
• Authors: Patruno, Cosimo; Marani, Roberto; Cicirelli, Grazia; Stella, Ettore; D'Orazio, Tiziana
• Is Part Of: Pattern recognition. Volume 89(2019:May)
• Page Start: 77
• Page End: 90
• Publication Date: 2019-05
• Subjects: People re-identification -- Color-based descriptor -- Skeleton standard posture -- Partition grid -- RGB-D sensor -- Color point cloud; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.01.003
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A people re-identification approach by color and depth data is proposed. Point clouds are aligned to generate pose-independent signatures. An unevenly-spaced partition grid is computed on the skeleton standard posture of the person under analysis. The partition grid drives a color-based descriptor extraction. The signatures of reference database are re-projected using the partition grid of the person under analysis to highlight shape differences. Abstract: This paper tackles the problem of people re-identification by using soft biometrics features. The method works on RGB-D data (color point clouds) to determine the best matching among a database of possible users. For each subject under testing, skeletal information in three-dimensions is used to regularize the pose and to create a skeleton standard posture (SSP). A partition grid, whose sizes depend on the SSP, groups the samples of the point cloud accordingly to their position. Every group is then studied to build the person signature. The same grid is then used for the other subjects of the database to preserve information about possible shape differences among users. The effectiveness of this novel method has been tested on three public datasets. Numerical experiments demonstrate an improvement of results with reference to the current state-of-the-art, with recognition rates of 97.84% (on a partition of BIWI RGBD-ID), 61.97% (KinectREID) and 89.71% (RGBD-ID), respectively.