Real-Time Object Detection with OpenCV and Python

This paper presents a real-time object detection system using OpenCV and Python. The proposed model aims to achieve fast, accurate, and reliable identification of objects from a live video stream. The system utilizes deep learning–based pretrained architectures such as MobileNet-SSD and YOLO-tiny, known for their efficiency in real-time detection tasks. The workflow includes frame acquisition, preprocessing, blob formation, and inference using OpenCV’s DNN module. Results demonstrate that lightweight models can process video frames in real time on CPU hardware while maintaining good accuracy. This study highlights how computer vision and deep learning can support automation, surveillance, and human–computer interaction systems.