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

Authors: P. Nagaraju; Manchala Sadanandam

Addresses: Department of Computer Science and Engineering, Kakatiya University, Warangal, Telangana, 506 009, India ' Department of Computer Science and Engineering, Kakatiya University, Warangal, Telangana, 506 009, India

Abstract: 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.

Keywords: CNN; convolutional neural network; faster R-CNN; network architecture; object motion detection; track moving objects; video sequence systems.

DOI: 10.1504/IJSSE.2026.153647

International Journal of System of Systems Engineering, 2026 Vol.16 No.2, pp.121 - 142

Received: 24 Aug 2023
Accepted: 11 Dec 2023
Published online: 21 May 2026 *

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