Optimizing motion detection performance: Harnessing the power of squeeze and excitation modules

Jabulani Brown Mpofu, Chenglong Li, Xinyan Gao and Xinxin Su

PLOS ONE, 2024, vol. 19, issue 8, 1-18

Abstract: This paper introduces an innovative segmentation model that extends the U-Net architecture with a Squeeze and Excitation (SE) mechanism, designed to enhance the detection of moving objects in video streams. By integrating this model into the ViBe motion detection algorithm, we have significantly improved detection accuracy and reduced false positive rates. Our approach leverages adaptive techniques to increase the robustness of the segmentation model in complex scenarios, without requiring extensive manual parameter tuning. Despite the notable improvements, we recognize that further training is necessary to optimize the model for specific applications. The results indicate that our method provides a promising direction for real-time motion detection systems that require high precision and adaptability to varying conditions.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308933 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 08933&type=printable (application/pdf)

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:plo:pone00:0308933

DOI: 10.1371/journal.pone.0308933

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().