Abnormality Identification in Video Surveillance System using DCT Article Swipe
A. Balasundaram
,
Golda Dilip
,
M Manickam
,
Arun Kumar Sivaraman
,
K. Gurunathan
,
R. Dhanalakshmi
,
S Ashokkumar
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.32604/iasc.2022.022241
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.32604/iasc.2022.022241
In the present world, video surveillance methods play a vital role in observing the activities that take place across secured and unsecured environment. The main aim with which a surveillance system is deployed is to spot abnormalities in specific areas like airport, military, forests and other remote areas, etc. A new block-based strategy is represented in this paper. This strategy is used to identify unusual circumstances by examining the pixel-wise frame movement instead of the standard object-based approaches. The density and also the speed of the movement is extorted by utilizing optical flow. The proposed strategy recognizes the unusual movement and differences by using discrete cosine transform coefficient. Our goal is to attain a trouble-free block-based Discrete Cosine Transform (DCT) strategy that promotes real-time abnormality detection. The proposed approach has been evaluated against an airport dataset and the outcome of unusual happenings occurred in is evaluated and reported.
Related Topics
Concepts
Discrete cosine transform
Computer science
Block (permutation group theory)
Abnormality
Computer vision
Artificial intelligence
Identification (biology)
Frame (networking)
Pixel
Object (grammar)
Optical flow
Computer security
Image (mathematics)
Telecommunications
Mathematics
Psychology
Biology
Social psychology
Botany
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/iasc.2022.022241
- https://www.techscience.com/iasc/v32n2/45602/pdf
- OA Status
- hybrid
- Cited By
- 22
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3214684863
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3214684863Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.32604/iasc.2022.022241Digital Object Identifier
- Title
-
Abnormality Identification in Video Surveillance System using DCTWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-17Full publication date if available
- Authors
-
A. Balasundaram, Golda Dilip, M Manickam, Arun Kumar Sivaraman, K. Gurunathan, R. Dhanalakshmi, S AshokkumarList of authors in order
- Landing page
-
https://doi.org/10.32604/iasc.2022.022241Publisher landing page
- PDF URL
-
https://www.techscience.com/iasc/v32n2/45602/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://www.techscience.com/iasc/v32n2/45602/pdfDirect OA link when available
- Concepts
-
Discrete cosine transform, Computer science, Block (permutation group theory), Abnormality, Computer vision, Artificial intelligence, Identification (biology), Frame (networking), Pixel, Object (grammar), Optical flow, Computer security, Image (mathematics), Telecommunications, Mathematics, Psychology, Biology, Social psychology, Botany, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
22Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 3, 2023: 4, 2022: 13Per-year citation counts (last 5 years)
- References (count)
-
20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 1, 13, 68, 74, 82, 85, 97, 137 |
| abstract_inverted_index.This | 58 |
| abstract_inverted_index.also | 81 |
| abstract_inverted_index.been | 130 |
| abstract_inverted_index.etc. | 48 |
| abstract_inverted_index.goal | 109 |
| abstract_inverted_index.like | 40 |
| abstract_inverted_index.main | 24 |
| abstract_inverted_index.play | 7 |
| abstract_inverted_index.role | 10 |
| abstract_inverted_index.spot | 35 |
| abstract_inverted_index.take | 16 |
| abstract_inverted_index.that | 15, 121 |
| abstract_inverted_index.this | 56 |
| abstract_inverted_index.used | 61 |
| abstract_inverted_index.with | 26 |
| abstract_inverted_index.(DCT) | 119 |
| abstract_inverted_index.areas | 39 |
| abstract_inverted_index.flow. | 92 |
| abstract_inverted_index.frame | 70 |
| abstract_inverted_index.other | 45 |
| abstract_inverted_index.place | 17 |
| abstract_inverted_index.speed | 83 |
| abstract_inverted_index.using | 103 |
| abstract_inverted_index.video | 4 |
| abstract_inverted_index.vital | 9 |
| abstract_inverted_index.which | 27 |
| abstract_inverted_index.Cosine | 117 |
| abstract_inverted_index.across | 18 |
| abstract_inverted_index.areas, | 47 |
| abstract_inverted_index.attain | 112 |
| abstract_inverted_index.cosine | 105 |
| abstract_inverted_index.paper. | 57 |
| abstract_inverted_index.remote | 46 |
| abstract_inverted_index.system | 30 |
| abstract_inverted_index.world, | 3 |
| abstract_inverted_index.against | 132 |
| abstract_inverted_index.airport | 134 |
| abstract_inverted_index.dataset | 135 |
| abstract_inverted_index.density | 79 |
| abstract_inverted_index.forests | 43 |
| abstract_inverted_index.instead | 72 |
| abstract_inverted_index.methods | 6 |
| abstract_inverted_index.optical | 91 |
| abstract_inverted_index.outcome | 138 |
| abstract_inverted_index.present | 2 |
| abstract_inverted_index.secured | 19 |
| abstract_inverted_index.unusual | 64, 98, 140 |
| abstract_inverted_index.Discrete | 116 |
| abstract_inverted_index.airport, | 41 |
| abstract_inverted_index.approach | 128 |
| abstract_inverted_index.deployed | 32 |
| abstract_inverted_index.discrete | 104 |
| abstract_inverted_index.extorted | 88 |
| abstract_inverted_index.identify | 63 |
| abstract_inverted_index.movement | 71, 86, 99 |
| abstract_inverted_index.occurred | 142 |
| abstract_inverted_index.promotes | 122 |
| abstract_inverted_index.proposed | 94, 127 |
| abstract_inverted_index.specific | 38 |
| abstract_inverted_index.standard | 75 |
| abstract_inverted_index.strategy | 52, 59, 95, 120 |
| abstract_inverted_index.Transform | 118 |
| abstract_inverted_index.evaluated | 131, 145 |
| abstract_inverted_index.examining | 67 |
| abstract_inverted_index.military, | 42 |
| abstract_inverted_index.observing | 12 |
| abstract_inverted_index.real-time | 123 |
| abstract_inverted_index.reported. | 147 |
| abstract_inverted_index.transform | 106 |
| abstract_inverted_index.unsecured | 21 |
| abstract_inverted_index.utilizing | 90 |
| abstract_inverted_index.activities | 14 |
| abstract_inverted_index.detection. | 125 |
| abstract_inverted_index.happenings | 141 |
| abstract_inverted_index.pixel-wise | 69 |
| abstract_inverted_index.recognizes | 96 |
| abstract_inverted_index.abnormality | 124 |
| abstract_inverted_index.approaches. | 77 |
| abstract_inverted_index.block-based | 51, 115 |
| abstract_inverted_index.differences | 101 |
| abstract_inverted_index.represented | 54 |
| abstract_inverted_index.coefficient. | 107 |
| abstract_inverted_index.environment. | 22 |
| abstract_inverted_index.object-based | 76 |
| abstract_inverted_index.surveillance | 5, 29 |
| abstract_inverted_index.trouble-free | 114 |
| abstract_inverted_index.abnormalities | 36 |
| abstract_inverted_index.circumstances | 65 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.92301898 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |