THE MODEL OF THE SYSTEM FOR OBJECTS RECOGNITION IN THE REAL-TIME VIDEO STREAM

Abstract

This study addresses the development of a robust object recognition system tailored for real-time video streams. With the increasing integration of mobile devices in diverse applications, the research focuses on leveraging temporal and spatial data inherent in video streams to mitigate challenges such as environmental noise, preprocessing defects, and algorithmic errors. The proposed system incorporates dynamic models and convolutional neural networks (CNNs) to enhance recognition accuracy. Experimental evaluations using various datasets demonstrate the efficacy of combining classifier outputs and applying integration strategies suited for mobile platforms. The findings have practical implications for automated document processing, security systems, and mobile technology advancements, contributing to the broader field of computer vision.