Moving object detection based on frame difference and W4
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In [72], the proposed method improved accuracy by 2% to 4% compared with inter-frame and three-frame difference methods across three datasets.
“The findings revealed that, in various environments, the proposed method exhibited an improvement in accuracy ranging from 2% to 4% when contrasted with the inter-frame and three-frame difference methods.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [72]
In [72], variations in accuracy were influenced by the distance between the camera and the object being tracked and by the speed of the object's movement.
“These variations in accuracy were influenced by factors such as the distance between the camera and the object being tracked, as well as the speed of the object's movement.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [72]
In [72], the highest accuracy was achieved in tracking people during meetings, and the least accurate results were in tracking traffic.
“Notably, the highest accuracy was achieved in tracking people during meetings, while the least accurate results were observed in the context of tracking traffic.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [72]
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A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134 · see citing works
“In [72], the authors introduced the frame differencing and W4 algorithm for detecting moving pedestrians and vehicles in a noisy environment with a complex background.”
Background
- Authors
- Sengar, S. S., & Mukhopadhyay, S.
- Affiliations
- Indian Institute of Technology Dhanbad (IN)
- Corresponding author
- Sengar, Sandeep Singh
- Venue
- Signal Image and Video Processing
- Published
- 2017-04-17
- Volume
- 11
- Issue
- 7
- Pages
- 1357-1364
- ISSN
- 1863-1703 (ISSN-L)
- 1863-1711
- OpenAlex
- W2605847739
- Cited by
- 78
- References
- 30
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 6 · 2023: 17 · 2022: 11 · 2021: 10 · 2020: 6 · 2019: 9 · 2018: 7 · 2017: 2
- FWCI
- 2.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Signal Image and Video Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Aerospace Engineering › Infrared Target Detection Methodologies
- Physical Sciences › Computer Science › Signal Processing › Video Coding and Compression Technologies
- Keywords
- moving object detection, frame difference, video surveillance, foreground segmentation, ghosting, connected component labeling, morphological operations
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