Performance analysis of surveillance video object detection using LUNET algorithm

T. Mohandoss () and J. Rangaraj ()
Additional contact information
T. Mohandoss: Annamalai University
J. Rangaraj: Annamalai University

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 7, No 14, 3026 pages

Abstract: Abstract Object detection algorithms have applications in various fields, including security, healthcare and defense. Because image-based object detection cannot exploit the rich temporal information inherent in video data, we suggest long-range video object pattern detection. Standard video-based object detectors use temporal context information to enhance object detection efficiency. However, object detection in challenging environments has received little attention. This paper proposes an improved You Only Look Once version 2 (YOLOv2) algorithms for object detection in surveillance videos, specifically vehicle detection and recognition. We reduced the number of parameters in the YOLOv2 base network and replaced it with LuNet. In the enhanced model, by using LuNet model for feature extraction to extract the most representative features from the image. LuNet is unique neural network architecture, a traditional and very promising algorithm for solving machine learning problems in video data frames. We perform numerous tests to evaluate the efficiency of the suggested approach, and our method outperforms conventional vehicle detection methods with an average accuracy of 96.41%. The study's findings demonstrate that the suggested technique achieves higher f-measure, precision, and error rate than other approaches.

Keywords: Video object detection; Deep learning; LuNet; Intersection over union (IoU) (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-024-02311-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:15:y:2024:i:7:d:10.1007_s13198-024-02311-0

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-024-02311-0

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().