Abnormal events detection using spatio-temporal saliency descriptor and fuzzy representation analysis
Abstract In recent years, the research on abnormal events detection is a significant work in surveillance video. Many researchers have been attracted by this work for the past two decades. As a result, several abnormal event detection approaches have been developed. Though several approaches have be...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81387-x |
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| author | R. Tino Merlin R. Karthick A. Aalan Babu G. Vennira Selvi D. Usha R. Nithya |
| author_facet | R. Tino Merlin R. Karthick A. Aalan Babu G. Vennira Selvi D. Usha R. Nithya |
| author_sort | R. Tino Merlin |
| collection | DOAJ |
| description | Abstract In recent years, the research on abnormal events detection is a significant work in surveillance video. Many researchers have been attracted by this work for the past two decades. As a result, several abnormal event detection approaches have been developed. Though several approaches have been used in the field still many problems remain to get the abnormal events detection accuracy. Moreover, many feature representations have limited capability to describe the content since several research works applied hand craft features, this type of feature can work in limited problems. To overcome this problem, this paper introduced the novel feature descriptor namely STS-D (Spatial and Temporal Saliency - Descriptor), which includes spatial and temporal information of the objects. This feature descriptor efficiently describes the shape and speed of the object. To find the anomaly score, fuzzy representation is modeled to efficiently differentiate the normal and abnormal events using fuzzy membership degree. The benchmark datasets UMN, UCSD Ped1 and Ped2 and real time roadway surveillance dataset are used to evaluate the performance of the proposed approach. Also, several existing abnormal events detection approaches are used to compare with the proposed method to evaluate the effectiveness of the proposed work. |
| format | Article |
| id | doaj-art-156627e3df69435eb15511ef2cf18ddd |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-156627e3df69435eb15511ef2cf18ddd2025-08-20T02:49:15ZengNature PortfolioScientific Reports2045-23222024-11-0114111210.1038/s41598-024-81387-xAbnormal events detection using spatio-temporal saliency descriptor and fuzzy representation analysisR. Tino Merlin0R. Karthick1A. Aalan Babu2G. Vennira Selvi3D. Usha4R. Nithya5Department of Computer Science and Engineering, Francis Xavier Engineering CollegeDepartment of Computer Science and Engineering, Dr. Mahalingam College of Engineering and TechnologyDepartment of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologySchool of Computer Science and Engineering and Information Science, Presidency UniversityDepartment of Computer Science, Mother Teresa Women’s UniversityDepartment of Computer Science and Engineering, Bannari Amman Institute of Technology SathyamangalamAbstract In recent years, the research on abnormal events detection is a significant work in surveillance video. Many researchers have been attracted by this work for the past two decades. As a result, several abnormal event detection approaches have been developed. Though several approaches have been used in the field still many problems remain to get the abnormal events detection accuracy. Moreover, many feature representations have limited capability to describe the content since several research works applied hand craft features, this type of feature can work in limited problems. To overcome this problem, this paper introduced the novel feature descriptor namely STS-D (Spatial and Temporal Saliency - Descriptor), which includes spatial and temporal information of the objects. This feature descriptor efficiently describes the shape and speed of the object. To find the anomaly score, fuzzy representation is modeled to efficiently differentiate the normal and abnormal events using fuzzy membership degree. The benchmark datasets UMN, UCSD Ped1 and Ped2 and real time roadway surveillance dataset are used to evaluate the performance of the proposed approach. Also, several existing abnormal events detection approaches are used to compare with the proposed method to evaluate the effectiveness of the proposed work.https://doi.org/10.1038/s41598-024-81387-xSpatio-temporal descriptorFuzzy representationInfluence scoreAnd abnormal events detection |
| spellingShingle | R. Tino Merlin R. Karthick A. Aalan Babu G. Vennira Selvi D. Usha R. Nithya Abnormal events detection using spatio-temporal saliency descriptor and fuzzy representation analysis Scientific Reports Spatio-temporal descriptor Fuzzy representation Influence score And abnormal events detection |
| title | Abnormal events detection using spatio-temporal saliency descriptor and fuzzy representation analysis |
| title_full | Abnormal events detection using spatio-temporal saliency descriptor and fuzzy representation analysis |
| title_fullStr | Abnormal events detection using spatio-temporal saliency descriptor and fuzzy representation analysis |
| title_full_unstemmed | Abnormal events detection using spatio-temporal saliency descriptor and fuzzy representation analysis |
| title_short | Abnormal events detection using spatio-temporal saliency descriptor and fuzzy representation analysis |
| title_sort | abnormal events detection using spatio temporal saliency descriptor and fuzzy representation analysis |
| topic | Spatio-temporal descriptor Fuzzy representation Influence score And abnormal events detection |
| url | https://doi.org/10.1038/s41598-024-81387-x |
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