An Overview of Moving Object Detection Using YOLO Deep Learning Models

• Published in: 2024 2nd International Conference on Disruptive Technologies (ICDT) pp. 1014 - 1020
• Main Authors: Dwivedi, Upendra, Joshi, Kireet, Shukla, Surendra Kumar, Rajawat, Anand Singh
• Format: Conference Proceeding
• Language: English
• Published: IEEE 15-03-2024
• Subjects: Computational modeling; Computer vision; Deep learning; Image Processing; Motion Detection; Moving Object Detection; Prediction algorithms; Training data; Visualization; YOLO
• DOI: 10.1109/ICDT61202.2024.10489800

Computer vision is a promising domain that focuses on emerging approaches, algorithms and technologies to provide computing capability to machine to analysis visual data, such as image files, videos files and real time video streaming. In Computer Vision and image processing detecting object from images and videos has been topic of extensive research. This is accomplished by applying various computer vision techniques to analyze the visual data and determine class of objects from image and videos files. One widespread approach to object detection is using the deep learning models. YOLO (You Only Look Once) is a convolutional neural network that offers a fast and efficient solution to object detection using deep learning. It enable computer to handle real-time detection of objects in video frames and accurately locate and classify moving objects. YOLO is able to simultaneously detect and classify objects in an efficient manner using convolutional neural networks.