A Study of YOLO-Based Object Detection for Visually Impaired Individuals

Abstract

An essential area of investigation involves recognizing and tracking objects, given the frequent changes in object motion, scene dimensions, occlusions, appearance, ego-motion, and lighting variations. Effective object tracking heavily relies on feature selection due to its critical role. This process is integral to numerous real-time applications like vehicle detection and video surveillance. To address detection challenges, tracking methods need to be adept at handling object movement and appearance changes. Among these methods, tracking algorithms play a pivotal role in smoothing video sequences and receive significant attention. However, only a few approaches leverage previously gathered data on object attributes such as shape and color. This study delves into a tracking algorithm that encompasses all these object properties. Its aim is to investigate and assess past methodologies for tracking and detecting moving objects across various stages of video sequences. Additionally, it aims to identify existing challenges and propose innovative strategies to enhance object tracking throughout video frames