Abstract:

Detecting the existence, position, and posture of objects in an image or video is the task of object detection, a branch of computer vision. Real Time Objects. It is a crucial task in many applications, such as robotics, self-driving cars, and surveillance systems. Object detection methods in machine learning often utilize a mix of feature extraction and classification approaches to identify things. The process starts by extracting relevant features from the input image or video, such as edges, corners, and textures. These features are then fed into a classifier, which uses them to predict the class label of the object (e.g., car, pedestrian, stop sign). Convolutional neural networks (CNNs) are often used to analyse images and find areas that contain objects as a standard method for object detection. The CNN may be trained on a sizable dataset of annotated pictures and then used to anticipate the presence and placement of objects in fresh photos and videos

Description:

Convolutional neural network (CNN), Real time objects, annotated images, Object Detection, Computer Vision