Modeling Real Time Object Identification Performance Evaluation with YOLO

Abstract

Object identification in pictures, videos, and signal processing is not a big frontier and has been around for a few years. Though object recognition in static pictures has proven highly promising in terms of specific items such as facial recognition systems, illness diagnosis, and so on, it has been tricky when it comes to video processing and real-time image processing. Traditional object identification techniques are coupled with machine learning methodologies to improve algorithms' speed and accuracy. The paper focuses on real-time object recognition systems and how recent advances in the realm of object recognition and identification have been made while bearing in mind the real-time scenarios for recognizing varied objects.The paper discusses the current implementations illustrated by varied authors using far more widely used algorithms including YOLO for real-time visualizations. Keywords: Object recognition, YOLO, Machine learning, RCNN, faster RCNN