LIVE OBJECT DETECTION USING OPENCV

Abstract

Object detection is one of the most basic and central task in computer vision. Its task is to find all the interested objects in the image, and determine the category and location of the objects. Object detection is widely used and has strong practical value and research prospects. Applications include face detection, pedestrian detection and vehicle detection. In recent years, with the development of convolutional neural network, significant breakthroughs have been made in object detection. The latest research in this field has been making tremendous development in many areas. Object detection and tracking have a variety of uses, this project presents a general trainable framework for object detection in images and videos including live video. The detection technique we are using is based on YOLO. In this project, we also discuss current and prospective applications of object detection in several fields. The results presented here suggest that this architecture can be further developed and used in face detection, face recognition, anomaly detection, crowd counting, security surveillance, etc. The Objective is to detect of objects using You Only Look Once (YOLO) approach. This method has several advantages as compared to other object detection algorithms. In other algorithms like Convolutional Neural Network, Fast Convolutional Neural Network the algorithm will not look at the image completely but in YOLO the algorithm looks the image completely by predicting the bounding boxes using convolutional network and the class probabilities for these boxes and detects the image faster as compared to other algorithms.