Abstract: This paper presents a real-time object detection system for visualizing and processing streaming media using OpenCV and the YOLOv5 deep learning model. The proposed system captures live video streams and processes each frame to accurately detect and classify multiple objects in real time. OpenCV is used for video acquisition, preprocessing, and visualization, while YOLOv5 performs fast and reliable object detection by generating bounding boxes and class labels. The system effectively detects objects such as persons, mobile phones, and clocks with high accuracy and smooth frame rates. Experimental results demonstrate that the proposed approach achieves a good balance between detection accuracy and real-time performance on standard laptop hardware. The developed system is scalable and can be extended to advanced applications such as surveillance, traffic monitoring, and intelligent video analytics.
Keywords: OpenCV, YOLOv5, Object Detection, Computer Vision, Deep Learning, Real-Time Video Processing, Image Processing.
Title: Visualizing and Processing Streaming Media with Object Identification Using OpenCV
Author: Sujan C R, Veeresh I Hiremath, Vishal K, Deepika D Pai
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Vol. 14, Issue 1, January 2026 - March 2026
Page No: 122-127
Research Publish Journals
Website: www.researchpublish.com
Published Date: 02-March-2026
