Version 1
: Received: 20 December 2023 / Approved: 20 December 2023 / Online: 21 December 2023 (13:03:22 CET)
How to cite:
Mumtaz, F.; Ashraf, H.; Jhanjhi, N. Moving Object Detection Using Deep Learning Algorithms. Preprints2023, 2023121589. https://doi.org/10.20944/preprints202312.1589.v1
Mumtaz, F.; Ashraf, H.; Jhanjhi, N. Moving Object Detection Using Deep Learning Algorithms. Preprints 2023, 2023121589. https://doi.org/10.20944/preprints202312.1589.v1
Mumtaz, F.; Ashraf, H.; Jhanjhi, N. Moving Object Detection Using Deep Learning Algorithms. Preprints2023, 2023121589. https://doi.org/10.20944/preprints202312.1589.v1
APA Style
Mumtaz, F., Ashraf, H., & Jhanjhi, N. (2023). Moving Object Detection Using Deep Learning Algorithms. Preprints. https://doi.org/10.20944/preprints202312.1589.v1
Chicago/Turabian Style
Mumtaz, F., Humaira Ashraf and NZ Jhanjhi. 2023 "Moving Object Detection Using Deep Learning Algorithms" Preprints. https://doi.org/10.20944/preprints202312.1589.v1
Abstract
Moving object detection plays a crucial role in various applications, particularly in traffic surveillance and collision warning systems. However, the high cost of training and slow detection pose significant challenges to the practicality and real-time performance of existing methods. These challenges hinder the practicality and real-time performance of existing methods. The need for a reliable and efficient moving object detection system prompts the search for cost-effective strategies. Enhancing detection speed without sacrificing accuracy is crucial for real-time applications. Overcoming these challenges is essential to advance the field of moving object detection and improve its applicability in various domains. This paper presents a comprehensive review of the literature on moving object detection, proposes novel strategies to overcome the challenges of high training costs and slow detection. The study emphasizes the importance of reliable speed monitoring systems and advancements in control systems to enhance driver assistance on urban highways. Each frame in the dataset undergoes YOLOv6 processing to detect and classify objects, generating bounding box predictions and class probabilities. Optical flow, specifically the Lucas-Kanade method, is then utilized to compute motion vectors between consecutive frames. These motion vectors facilitate object tracking across frames, enabling refined bounding box positions and improving overall object tracking accuracy. The results demonstrate the effectiveness of the proposed technique in reducing training costs, improving detection speed, and maintaining a high level of accuracy when compared to existing methods. The paper concludes by summarizing the findings and highlighting the potential of the proposed strategy for precise and reliable moving object recognition in real-world traffic scenarios
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
