Review: Multiple Objects Detection and Tracking using Deep Learning Approach

Abstract

Multiple Object Tracking (MOT) is a crucial tool with diverse applications, such as object detection, object counting, and security systems. Precise identification and monitoring of numerous objects are essential in several computer vision uses, such as monitoring, self-driving cars, and computer-human communication. Very little has been done to address occlusion problems in order to enable the best moving object tracking with detection The tracking of visual objects is one of the most important components of computer vision. The process of tracking an object (or a group of objects) across time is called object tracking. Visual object tracking is used to identify or link target items over successive video frames. In this study, we analyze the tracking-by-detection strategy, which includes YOLO-based detection and SORT-based tracking. This work elucidates a general approach to tracking and recognizing many objects with an emphasis on accuracy improvement. We aim to revolutionize computer vision by applying Non-Maximum Suppression (NMS) and Intersection over Union (IoU) approaches, and by combining the state-of-the-art YOLO NAS algorithm with conventional tracking methods or an alternative version of the YOLO Algorithm for object identification. It is expected that our work will have a major impact on many different applications, enabling more precise and reliable object tracking and detection in difficult real-world scenarios. Keywords—Multiple Object Detection, Kalman Filters Multiple Object Tracking, DeepSORT, YOLO, IoU, NMS.