Detection and Localization of Abnormal Events for Smart Surveillance Article Swipe
Baliram Sambhaji Gayal
,
Sandip Raosaheb Patil
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.18280/isi.270207
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.18280/isi.270207
In this study, the methods of anomaly detection are proposed. Background substitution (BG) is used for extracting the motion and indicating the attention region's locations, which are employed. Then the regions are fed into the “Deep Convolutional Neural Network (DCNN)”. With the advantages of DCNN, for properly exploiting the spatiotemporal relationships, a network is developed for distinguishing anomalous and normal events. Besides this, the anomaly detection techniques are also described. The related databases are provided in this study. Many techniques for anomaly detection are discussed in this study with the help of the neural network. The different types of anomaly events are discussed here. All the data related to these anomaly events are discussed in the dataset. Different types of models related to the CNN model are also discussed in this study. And the anomaly techniques are also considered for discussion in this study.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18280/isi.270207
- https://www.iieta.org/download/file/fid/73944
- OA Status
- bronze
- Cited By
- 2
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4280555209
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4280555209Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18280/isi.270207Digital Object Identifier
- Title
-
Detection and Localization of Abnormal Events for Smart SurveillanceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-30Full publication date if available
- Authors
-
Baliram Sambhaji Gayal, Sandip Raosaheb PatilList of authors in order
- Landing page
-
https://doi.org/10.18280/isi.270207Publisher landing page
- PDF URL
-
https://www.iieta.org/download/file/fid/73944Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://www.iieta.org/download/file/fid/73944Direct OA link when available
- Concepts
-
Anomaly detection, Anomaly (physics), Convolutional neural network, Computer science, Artificial intelligence, Artificial neural network, Pattern recognition (psychology), Data mining, Physics, Condensed matter physicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4280555209 |
|---|---|
| doi | https://doi.org/10.18280/isi.270207 |
| ids.doi | https://doi.org/10.18280/isi.270207 |
| ids.openalex | https://openalex.org/W4280555209 |
| fwci | 0.39159764 |
| type | article |
| title | Detection and Localization of Abnormal Events for Smart Surveillance |
| biblio.issue | 2 |
| biblio.volume | 27 |
| biblio.last_page | 241 |
| biblio.first_page | 233 |
| topics[0].id | https://openalex.org/T11512 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9994000196456909 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Anomaly Detection Techniques and Applications |
| topics[1].id | https://openalex.org/T10400 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9926000237464905 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1705 |
| topics[1].subfield.display_name | Computer Networks and Communications |
| topics[1].display_name | Network Security and Intrusion Detection |
| topics[2].id | https://openalex.org/T10917 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.930899977684021 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2207 |
| topics[2].subfield.display_name | Control and Systems Engineering |
| topics[2].display_name | Smart Grid Security and Resilience |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C739882 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8867444396018982 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q3560506 |
| concepts[0].display_name | Anomaly detection |
| concepts[1].id | https://openalex.org/C12997251 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7656881213188171 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q567560 |
| concepts[1].display_name | Anomaly (physics) |
| concepts[2].id | https://openalex.org/C81363708 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6567743420600891 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[2].display_name | Convolutional neural network |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.63908451795578 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5579092502593994 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C50644808 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4442235827445984 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[5].display_name | Artificial neural network |
| concepts[6].id | https://openalex.org/C153180895 |
| concepts[6].level | 2 |
| concepts[6].score | 0.44391539692878723 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[6].display_name | Pattern recognition (psychology) |
| concepts[7].id | https://openalex.org/C124101348 |
| concepts[7].level | 1 |
| concepts[7].score | 0.35299813747406006 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[7].display_name | Data mining |
| concepts[8].id | https://openalex.org/C121332964 |
| concepts[8].level | 0 |
| concepts[8].score | 0.05936112999916077 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[8].display_name | Physics |
| concepts[9].id | https://openalex.org/C26873012 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q214781 |
| concepts[9].display_name | Condensed matter physics |
| keywords[0].id | https://openalex.org/keywords/anomaly-detection |
| keywords[0].score | 0.8867444396018982 |
| keywords[0].display_name | Anomaly detection |
| keywords[1].id | https://openalex.org/keywords/anomaly |
| keywords[1].score | 0.7656881213188171 |
| keywords[1].display_name | Anomaly (physics) |
| keywords[2].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[2].score | 0.6567743420600891 |
| keywords[2].display_name | Convolutional neural network |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.63908451795578 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.5579092502593994 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[5].score | 0.4442235827445984 |
| keywords[5].display_name | Artificial neural network |
| keywords[6].id | https://openalex.org/keywords/pattern-recognition |
| keywords[6].score | 0.44391539692878723 |
| keywords[6].display_name | Pattern recognition (psychology) |
| keywords[7].id | https://openalex.org/keywords/data-mining |
| keywords[7].score | 0.35299813747406006 |
| keywords[7].display_name | Data mining |
| keywords[8].id | https://openalex.org/keywords/physics |
| keywords[8].score | 0.05936112999916077 |
| keywords[8].display_name | Physics |
| language | en |
| locations[0].id | doi:10.18280/isi.270207 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S1757003 |
| locations[0].source.issn | 1633-1311, 2116-7125 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1633-1311 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Ingénierie des systèmes d information |
| locations[0].source.host_organization | https://openalex.org/P4310311056 |
| locations[0].source.host_organization_name | Lavoisier publishing |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310311056 |
| locations[0].source.host_organization_lineage_names | Lavoisier publishing |
| locations[0].license | |
| locations[0].pdf_url | https://www.iieta.org/download/file/fid/73944 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Ingénierie des systèmes d information |
| locations[0].landing_page_url | https://doi.org/10.18280/isi.270207 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5038679429 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-9177-2422 |
| authorships[0].author.display_name | Baliram Sambhaji Gayal |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I878213199 |
| authorships[0].affiliations[0].raw_affiliation_string | Dept. of Electronics & Telecommunication Engineering, JSPM's Rajarshi Shahu College of Engineering, Tathawade, Pune-33, India |
| authorships[0].institutions[0].id | https://openalex.org/I878213199 |
| authorships[0].institutions[0].ror | https://ror.org/044g6d731 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I878213199 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Savitribai Phule Pune University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Baliram Sambhaji Gayal |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Dept. of Electronics & Telecommunication Engineering, JSPM's Rajarshi Shahu College of Engineering, Tathawade, Pune-33, India |
| authorships[1].author.id | https://openalex.org/A5114014814 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Sandip Raosaheb Patil |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Sandip Raosaheb Patil |
| authorships[1].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.iieta.org/download/file/fid/73944 |
| open_access.oa_status | bronze |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Detection and Localization of Abnormal Events for Smart Surveillance |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11512 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9994000196456909 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Anomaly Detection Techniques and Applications |
| related_works | https://openalex.org/W3093612317, https://openalex.org/W2613736958, https://openalex.org/W2076520961, https://openalex.org/W2175746458, https://openalex.org/W2732542196, https://openalex.org/W2760085659, https://openalex.org/W2883200793, https://openalex.org/W2738221750, https://openalex.org/W3012978760, https://openalex.org/W4285195761 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2022 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.18280/isi.270207 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S1757003 |
| best_oa_location.source.issn | 1633-1311, 2116-7125 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1633-1311 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Ingénierie des systèmes d information |
| best_oa_location.source.host_organization | https://openalex.org/P4310311056 |
| best_oa_location.source.host_organization_name | Lavoisier publishing |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310311056 |
| best_oa_location.source.host_organization_lineage_names | Lavoisier publishing |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://www.iieta.org/download/file/fid/73944 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Ingénierie des systèmes d information |
| best_oa_location.landing_page_url | https://doi.org/10.18280/isi.270207 |
| primary_location.id | doi:10.18280/isi.270207 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S1757003 |
| primary_location.source.issn | 1633-1311, 2116-7125 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1633-1311 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Ingénierie des systèmes d information |
| primary_location.source.host_organization | https://openalex.org/P4310311056 |
| primary_location.source.host_organization_name | Lavoisier publishing |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310311056 |
| primary_location.source.host_organization_lineage_names | Lavoisier publishing |
| primary_location.license | |
| primary_location.pdf_url | https://www.iieta.org/download/file/fid/73944 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Ingénierie des systèmes d information |
| primary_location.landing_page_url | https://doi.org/10.18280/isi.270207 |
| publication_date | 2022-04-30 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2978367588, https://openalex.org/W2061438946, https://openalex.org/W2625270925, https://openalex.org/W2951031961, https://openalex.org/W2126050191, https://openalex.org/W3042680816, https://openalex.org/W3041107669, https://openalex.org/W2181684026, https://openalex.org/W4231906844, https://openalex.org/W2756277826, https://openalex.org/W3211147476, https://openalex.org/W2765825392, https://openalex.org/W3193463513, https://openalex.org/W4210466166, https://openalex.org/W2992704659, https://openalex.org/W2734751122, https://openalex.org/W3013827867, https://openalex.org/W3015832418, https://openalex.org/W2606144683, https://openalex.org/W2748303136, https://openalex.org/W3091235251, https://openalex.org/W3044374951, https://openalex.org/W2804149489, https://openalex.org/W3112836416, https://openalex.org/W2909081203, https://openalex.org/W2893511508, https://openalex.org/W3122069467, https://openalex.org/W3017679580, https://openalex.org/W3087909056, https://openalex.org/W2793657457, https://openalex.org/W2973962176, https://openalex.org/W2807862495, https://openalex.org/W1964846093, https://openalex.org/W3016705886, https://openalex.org/W3077420696, https://openalex.org/W3114427951, https://openalex.org/W2963215781, https://openalex.org/W2913427224, https://openalex.org/W4288332894, https://openalex.org/W2767427757 |
| referenced_works_count | 40 |
| abstract_inverted_index.a | 51 |
| abstract_inverted_index.In | 0 |
| abstract_inverted_index.in | 75, 85, 114, 129, 141 |
| abstract_inverted_index.is | 13, 53 |
| abstract_inverted_index.of | 5, 43, 91, 98, 119 |
| abstract_inverted_index.to | 108, 122 |
| abstract_inverted_index.All | 104 |
| abstract_inverted_index.And | 132 |
| abstract_inverted_index.CNN | 124 |
| abstract_inverted_index.The | 70, 95 |
| abstract_inverted_index.and | 19, 58 |
| abstract_inverted_index.are | 8, 26, 31, 67, 73, 83, 101, 112, 126, 136 |
| abstract_inverted_index.fed | 32 |
| abstract_inverted_index.for | 15, 45, 55, 80, 139 |
| abstract_inverted_index.the | 3, 17, 21, 29, 34, 41, 48, 63, 89, 92, 105, 115, 123, 133 |
| abstract_inverted_index.(BG) | 12 |
| abstract_inverted_index.Many | 78 |
| abstract_inverted_index.Then | 28 |
| abstract_inverted_index.With | 40 |
| abstract_inverted_index.also | 68, 127, 137 |
| abstract_inverted_index.data | 106 |
| abstract_inverted_index.help | 90 |
| abstract_inverted_index.into | 33 |
| abstract_inverted_index.this | 1, 76, 86, 130, 142 |
| abstract_inverted_index.used | 14 |
| abstract_inverted_index.with | 88 |
| abstract_inverted_index.DCNN, | 44 |
| abstract_inverted_index.here. | 103 |
| abstract_inverted_index.model | 125 |
| abstract_inverted_index.study | 87 |
| abstract_inverted_index.these | 109 |
| abstract_inverted_index.this, | 62 |
| abstract_inverted_index.types | 97, 118 |
| abstract_inverted_index.which | 25 |
| abstract_inverted_index.Neural | 37 |
| abstract_inverted_index.events | 100, 111 |
| abstract_inverted_index.models | 120 |
| abstract_inverted_index.motion | 18 |
| abstract_inverted_index.neural | 93 |
| abstract_inverted_index.normal | 59 |
| abstract_inverted_index.study, | 2 |
| abstract_inverted_index.study. | 77, 131, 143 |
| abstract_inverted_index.Besides | 61 |
| abstract_inverted_index.Network | 38 |
| abstract_inverted_index.anomaly | 6, 64, 81, 99, 110, 134 |
| abstract_inverted_index.events. | 60 |
| abstract_inverted_index.methods | 4 |
| abstract_inverted_index.network | 52 |
| abstract_inverted_index.regions | 30 |
| abstract_inverted_index.related | 71, 107, 121 |
| abstract_inverted_index.“Deep | 35 |
| abstract_inverted_index.dataset. | 116 |
| abstract_inverted_index.network. | 94 |
| abstract_inverted_index.properly | 46 |
| abstract_inverted_index.provided | 74 |
| abstract_inverted_index.region's | 23 |
| abstract_inverted_index.Different | 117 |
| abstract_inverted_index.anomalous | 57 |
| abstract_inverted_index.attention | 22 |
| abstract_inverted_index.databases | 72 |
| abstract_inverted_index.detection | 7, 65, 82 |
| abstract_inverted_index.developed | 54 |
| abstract_inverted_index.different | 96 |
| abstract_inverted_index.discussed | 84, 102, 113, 128 |
| abstract_inverted_index.employed. | 27 |
| abstract_inverted_index.proposed. | 9 |
| abstract_inverted_index.(DCNN)”. | 39 |
| abstract_inverted_index.Background | 10 |
| abstract_inverted_index.advantages | 42 |
| abstract_inverted_index.considered | 138 |
| abstract_inverted_index.described. | 69 |
| abstract_inverted_index.discussion | 140 |
| abstract_inverted_index.exploiting | 47 |
| abstract_inverted_index.extracting | 16 |
| abstract_inverted_index.indicating | 20 |
| abstract_inverted_index.locations, | 24 |
| abstract_inverted_index.techniques | 66, 79, 135 |
| abstract_inverted_index.substitution | 11 |
| abstract_inverted_index.Convolutional | 36 |
| abstract_inverted_index.distinguishing | 56 |
| abstract_inverted_index.relationships, | 50 |
| abstract_inverted_index.spatiotemporal | 49 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.60904005 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |