Paper Details

Name: Prateek Raj Srivastav, Raiyan Ahmad, Yuvraj Anand, Anchal Chauhan & Vanshika Jain
Country: India

Year: 2026
Volume: Volume No: 13, January, Year: 2026 (Special Issue)
Page Number: 362-368
DOI: https://doi.org/10.5281/zenodo.19016796

ABSTRACT—
Object detection and tracking are essential com- ponents of computer vision applications, from surveillance to autonomous systems. This paper introduces a real-time ob- ject detection and tracking system based on OpenCV, Python, and the
YOLO (You Only Look Once) algorithm. The sys- tem detects multiple objects in video streams efficiently and tracks their movement with high accuracy. Combining YOLO’s deep learning-driven detection with the tracking algorithms of OpenCV guarantees strong performance in challenging environ- ments. The system’s ability to deal with occlusions, lighting changes, and multiple object interactions is shown through experimental results. This work opens up the possibility for deep learning-based real-time vision applications and offers an extensible solution for automated monitoring and
inspection.

Index Terms—Object Detection, Tracking, YOLO, OpenCV, Python, Deep Learning, Computer Vision, Real- Time Processing, Autonomous Systems, Surveillance.