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

Abstract

Learning about appearance embedding is of great importance for a variety of different computer-vision applications, which has prompted a surge in person re-identification (Re-ID) papers. The aim of these papers has been to identify an individual over a set of non-overlapping cameras. Despite recent advances in RGB-RGB Re-ID approaches with deep-learning architectures, the approach fails to consistently work well when there are low resolutions in dark conditions. The introduction of different sensors (i.e., RGB-D and infrared (IR)) enables the capture of appearances even in dark conditions. Recently, a lot of research has been dedicated to addressing the issue of finding appearance embedding in dark conditions using different advanced camera sensors. In this paper, we give a comprehensive overview of existing Re-ID approaches that utilize the additional information from different sensor-based methods to address the constraints faced by RGB camera-based person Re-ID systems. Although there are a number of survey papers that consider either the RGB-RGB or Visible-IR scenarios, there are none that consider both RGB-D and RGB-IR. In this paper, we present a detailed taxonomy of the existing approaches along with the existing RGB-D and RGB-IR person Re-ID datasets. Then, we summarize the performance of state-of-the-art methods on several representative RGB-D and RGB-IR datasets. Finally, future directions and current issues are considered for improving the different sensor-based person Re-ID systems.