Improved Accuracy of Object Detection in Videos with a New Approach to Background Calculation

Abstract

Object detection in videos is a major challenge in computer vision. This paper presents an innovative approach to boosting object detection accuracy by combining clever truncated averaging for background computation with adaptive thresholding techniques based on fuzzy entropy and evolutionary difference (ED). The methodology offers a balanced solution, drawing on the advantages of both traditional and modern approaches to overcome persistent challenges.