An Improved Vibe Algorithm Based on Adaptive Thresholding and the Deep Learning-Driven Frame Difference Method Article Swipe
Huilin Liu
,
Huazhang Wei
,
Gaoming Yang
,
Chenxing Xia
,
Shenghui Zhao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12163481
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12163481
Foreground detection is the main way to identify regions of interest. The detection effectiveness determines the accuracy of subsequent behavior analysis. In order to enhance the detection effect and optimize the problems of low accuracy, this paper proposes an improved Vibe algorithm combining the frame difference method and adaptive thresholding. First, we adopt a shallow convolutional layer of VGG16 to extract the lower-level features of the image. Features images with high correlation are fused into a new image. Second, adaptive factors based on the spatio-temporal domain are introduced to divide the foreground and background. Finally, we construct an inter-frame average speed value to measure the moving speed of the foreground, which solves the mismatch problem between background change rate and model update rate. Experimental results show that our algorithm can effectively solve the drawback of the traditional method and prevent the background model from being contaminated. It suppresses the generation of ghosting, significantly improves detection accuracy, and reduces the false detection rate.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics12163481
- https://www.mdpi.com/2079-9292/12/16/3481/pdf?version=1692330338
- OA Status
- gold
- Cited By
- 2
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385948608
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385948608Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/electronics12163481Digital Object Identifier
- Title
-
An Improved Vibe Algorithm Based on Adaptive Thresholding and the Deep Learning-Driven Frame Difference MethodWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-17Full publication date if available
- Authors
-
Huilin Liu, Huazhang Wei, Gaoming Yang, Chenxing Xia, Shenghui ZhaoList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics12163481Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/12/16/3481/pdf?version=1692330338Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2079-9292/12/16/3481/pdf?version=1692330338Direct OA link when available
- Concepts
-
Thresholding, Ghosting, Computer science, Artificial intelligence, Frame (networking), Frame rate, Image (mathematics), Pattern recognition (psychology), Computer vision, Algorithm, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
29Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3035155487, https://openalex.org/W2904100413, https://openalex.org/W3110110343, https://openalex.org/W6810133507, https://openalex.org/W3035217138, https://openalex.org/W2592370397, https://openalex.org/W2218573654, https://openalex.org/W3154520359, https://openalex.org/W4210285115, https://openalex.org/W6635895324, https://openalex.org/W2125884443, https://openalex.org/W2912284589, https://openalex.org/W2911257413, https://openalex.org/W3003998673, https://openalex.org/W2971288653, https://openalex.org/W3032269385, https://openalex.org/W3099174094, https://openalex.org/W3207441107, https://openalex.org/W4205241707, https://openalex.org/W4323542889, https://openalex.org/W2893184996, https://openalex.org/W6796058891, https://openalex.org/W2127070222, https://openalex.org/W2605177509, https://openalex.org/W2096989099, https://openalex.org/W3048391978, https://openalex.org/W3006502309, https://openalex.org/W4221063280, https://openalex.org/W3168341664 |
| referenced_works_count | 29 |
| abstract_inverted_index.a | 53, 75 |
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| corresponding_author_ids | https://openalex.org/A5020566349 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I184681353 |
| citation_normalized_percentile.value | 0.55883054 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |