An Automated Partial Derivative Based Method for Detecting and Monitoring Moving Objects

Abstract

This work proposes a method for detecting and tracking moving objects that rely onthe partial differential equation technique and can track both forward and backward. In order to reduce the amount of noise in the output video, it is first divided into many frames and then pre-processed using methods for the Gaussian filters. The transfer function is calculated on the binarized frames following the acquisition of the absolute difference for forward tracking and backward tracking. The forward and backward tracking outputs are combined at the object tracking step to get the desired outcome. Statistics like f-measure, accuracy, retention, and precision are used to evaluate the predicted technique, and classic motion detection methods are also used to examine its effectiveness. According to the evaluation results, the suggested system is superior to the usual high-accuracy rate techniques.