YOLO Algorithm Implementation for Real Time Object Detection and Tracking

Nowadays modern society is over flooded with humongous masses of visual data. There exist many image analysis methods to dive into this sea of visual information. The constituents of these images and videos can be analyzed and further processed to recognize the useful information. The detection, identification, and localization of different objects could prove to be of mammoth use and can play a significant part in modern devices and technologies. This paper represents a comparative study of several entity recognizing methods like YOLO, Faster R-CNN and R-CNN over different parameters such as mAP, FPS, etc. This paper also introduces an intelligent system (robot) that is capable of localizing an object and following it in real-time. The required input image is provided by the ESP32 cam module which can be mounted on the robot. Machine Learning Algorithms are used for object detection. Position coordinates received are then used to locate, track and follow the moving object. Furthermore, it is of interest as it can scale down human tasks and help mortals to be aware of minute details about certain objects.