Performance comparison of optical flow and background subtraction and discrete wavelet transform methods for moving objects

Abstract

Self-driving cars and other autonomous vehicles rely on systems that can recognize and follow objects. The ways help people make safe decisions and navigate by showing things like people, cars, obstacles, and traffic lights. Computer vision algorithms encompass both object detection and tracking. Different methods are specifically developed for picture or video analysis not only to identify items within the visual content but also to accurately determine their precise locations. This can operate independently as an algorithm or as a constituent of an item-tracking system. Object tracking algorithms can be used to follow objects over video frames, providing a contrasting approach. The research article focuses on the mathematical model simulation of optical flow, background subtraction, and discrete wavelet transform (DWT) methods for moving objects. The performance evaluation of the methods is done based on simulation response time, accuracy, sensitivity, and specificity doe several images in different environments. The DWT has shown optimal behavior in terms of the response time of 0.27 seconds, accuracy of 95.34 %, selectivity of 95.96 %, and specificity of 94.68 %.