An approach towards Real-Time Object Detector Using Open CV

Object detection has drawn a lot of academic focus in recent years due to its tight relationship with video analysis and picture intelligence. A computer vision technique called object detection is used to find and identify things in images and videos. Object detection accurately draws bounding boxes around discovered objects, informing us of the location of the requested object in the video or image. Before continuing, it is crucial to understand the difference between object detection and picture analysis because they are two concepts that are primarily confused by one another. Object detection works in tandem with other computer vision techniques like image segmentation and detection to help us comprehend and analyse images and videos. The project's major objective is to create a system that can identify items such as people, bicycles, cars, buses, and boats from an image or a stream of images that are fed to it in the form of previously recorded video or real-time input from the camera. The system will draw bounding boxes around the things it has detected. This article outlines a strategy for rationally using OpenCV, the Yolo algorithm, and Python to get decent machine learning results.