Develop a convolutional neural network architecture to accurately detect and track moving objects in video sequence systems

Object motion detection constitutes the initial crucial step in collecting data about moving objects. The research offered a precise video sequence system motion detection method using a novel object tracking and recognition method through faster region convolutional neural network (R-CNN) that enhances object detection accuracy. To associate things, looks and improved motion are used. The RoI pooling layer uses max pooling to create a compact feature map with a given spatial extent from all admissible region of interest features. The assessment findings demonstrate that the performance of existing work has improved by minimising identification transitions and segmentation. Visual examination, accuracy testing, and comparison with other methods were used to examine the suggested technique's detection outcomes. The proposed Project is implemented using Python software. The FRCNN architecture outperforms other conventional techniques, such as the R-CNN, convolutional neural network (CNN), and deep neural network (DNN), with an accuracy rate of 97.31%, demonstrating a greater effectiveness.