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Xiao, Y., Zhou, K., Cui, G., Jia, L., Fang, Z., Yang, X. and Xia, Q. (2021) Deep Learning for Occluded and Multi-Scale Pedestrian Detection: A Review. IET Image Processing, 15, 286-301.
https://doi.org/10.1049/ipr2.12042

has been cited by the following article:

• TITLE: An Application of RGBD-Based Skeleton Reconstruction for Pedestrian Detection and Occlusion Handling

  AUTHORS: Ziyuan Liu

  KEYWORDS: AR, Pedestrian Detection, Occlusion Management, RGB-D, Azure Kinect, Unity

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.1, January 31, 2024

  ABSTRACT: This study explores the challenges posed by pedestrian detection and occlusion in AR applications, employing a novel approach that utilizes RGB-D-based skeleton reconstruction to reduce the overhead of classical pedestrian detection algorithms during training. Furthermore, it is dedicated to addressing occlusion issues in pedestrian detection by using Azure Kinect for body tracking and integrating a robust occlusion management algorithm, significantly enhancing detection efficiency. In experiments, an average latency of 204 milliseconds was measured, and the detection accuracy reached an outstanding level of 97%. Additionally, this approach has been successfully applied in creating a simple yet captivating augmented reality game, demonstrating the practical application of the algorithm.