Enhancing Object Detection in Smart Video Surveillance: A Survey of Occlusion-Handling Approaches

Abstract

Smart video surveillance systems (SVSs) have garnered significant attention for their autonomous monitoring capabilities, encompassing automated detection, tracking, analysis, and decision making within complex environments, with minimal human intervention. In this context, object detection is a fundamental task in SVS. However, many current approaches often overlook occlusion by nearby objects, posing challenges to real-world SVS applications. To address this crucial issue, this paper presents a comprehensive comparative analysis of occlusion-handling techniques tailored for object detection. The review outlines the pretext tasks common to both domains and explores various architectural solutions to combat occlusion. Unlike prior studies that primarily focus on a single dataset, our analysis spans multiple benchmark datasets, providing a thorough assessment of various object detection methods. By extending the evaluation to datasets beyond the KITTI benchmark, this study offers a more holistic understanding of each approach’s strengths and limitations. Additionally, we delve into persistent challenges in existing occlusion-handling approaches and emphasize the need for innovative strategies and future research directions to drive substantial progress in this field.

An Advanced Approach to Object Detection and Tracking in Robotics and Autonomous Vehicles Using YOLOv8 and LiDAR Data Fusion

“…Object detection and tracking are important concepts in robotics [1][2][3] and self-driving [4,5]. There are several applications of object detection and tracking, such as in robotics for obstacle detection [5,6], in retail for tracking customer movement [7], in sports for analyzing player movements [8], and in security systems for monitoring and surveillance [9]. Object detection and tracking are important components in the fields of robotics and self-driving vehicles.…”