Fusion in Dissimilarity Space Between RGB D and Skeleton for Person Re Identification

Abstract

Person re-identification (Re-id) is one of the important tools of video surveillance systems, which aims to recognize an individual across the multiple disjoint sensors of a camera network. Despite the recent advances on RGB camera-based person re-identification methods under normal lighting conditions, Re-id researchers fail to take advantages of modern RGB-D sensor-based additional information (e.g. depth and skeleton information). When traditional RGB-based cameras fail to capture the video under poor illumination conditions, RGB-D sensor-based additional information can be advantageous to tackle these constraints. This work takes depth images and skeleton joint points as additional information along with RGB appearance cues and proposes a person re-identification method. We combine 4-channel RGB-D image features with skeleton information using score-level fusion strategy in dissimilarity space to increase re-identification accuracy. Moreover, our propose method overcomes the illumination problem because we use illumination invariant depth image and skeleton information. We carried out rigorous experiments on two publicly available RGBD-ID re-identification datasets and proved the use of combined features of 4-channel RGB-D images and skeleton information boost up the rank 1 recognition accuracy.

Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey

“…After the arrival of modern RGB–D and infrared sensors, Re-ID researchers took advantage of other modalities, such as depth and IR images, and skeleton information to increase the accuracy of Re-ID. In the past few years, a significant number of works have been proposed to combine RGB appearance cues with depth and/or skeleton information to extract more discriminative features [ 55 , 56 ]. Some works, for example [ 44 ], utilized RGB, depth, and thermal information together to improve the performance of Re-ID.…”