Autoencoder‐based abnormal activity detection using parallelepiped spatio‐temporal region
The spread of surveillance cameras has necessitated the monitoring of large quantities of surveillance video feeds. A manual monitoring system is near impossible due to the large man‐hour requirements. Recently, automatic abnormal activity detection has been an area of interest among researchers. A...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-02-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2018.5240 |
| _version_ | 1797684812083888128 |
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| author | Michael George Babita Roslind Jose Jimson Mathew Pranjali Kokare |
| author_facet | Michael George Babita Roslind Jose Jimson Mathew Pranjali Kokare |
| author_sort | Michael George |
| collection | DOAJ |
| description | The spread of surveillance cameras has necessitated the monitoring of large quantities of surveillance video feeds. A manual monitoring system is near impossible due to the large man‐hour requirements. Recently, automatic abnormal activity detection has been an area of interest among researchers. A spatio‐temporal feature, histogram of optical flow orientation and magnitude (HOFM), has produced impressive ability in detecting abnormal activities. The authors propose a novel non‐uniform spatio‐temporal region resembling parallelepipeds, from which they extract the HOFM features. Autoencoders can be configured to detect abnormal patterns. The authors have used these abilities of the autoencoders to detect abnormalities in the HOFM features extracted from their novel spatio‐temporal regions of the video feeds. The autoencoders are trained on the HOFM features of the videos containing no abnormalities. The autoencoders are then fed with the HOFM features of the videos to be tested for abnormal activities, and these are detected based on the abilities of the autoencoders to reconstruct these features. The proposed method is tested on the standard abnormality detection datasets: UCSD Ped1, UCSD Ped2, Subway Entrance, Subway Exit, and UMN. |
| first_indexed | 2024-03-12T00:35:11Z |
| format | Article |
| id | doaj.art-76d379861c9a4c20b2a1fbbc0eca0afd |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:35:11Z |
| publishDate | 2019-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-76d379861c9a4c20b2a1fbbc0eca0afd2023-09-15T09:51:44ZengWileyIET Computer Vision1751-96321751-96402019-02-01131233010.1049/iet-cvi.2018.5240Autoencoder‐based abnormal activity detection using parallelepiped spatio‐temporal regionMichael George0Babita Roslind Jose1Jimson Mathew2Pranjali Kokare3Division of Electronics and Communication Engg.School of Engineering, Cochin University of Science and TechnologyKochi682022IndiaDivision of Electronics and Communication Engg.School of Engineering, Cochin University of Science and TechnologyKochi682022IndiaDepartment of Computer Science and EngineeringIndian Institute of Technology PatnaPatna801103IndiaDepartment of Computer Science and EngineeringIndian Institute of Technology PatnaPatna801103IndiaThe spread of surveillance cameras has necessitated the monitoring of large quantities of surveillance video feeds. A manual monitoring system is near impossible due to the large man‐hour requirements. Recently, automatic abnormal activity detection has been an area of interest among researchers. A spatio‐temporal feature, histogram of optical flow orientation and magnitude (HOFM), has produced impressive ability in detecting abnormal activities. The authors propose a novel non‐uniform spatio‐temporal region resembling parallelepipeds, from which they extract the HOFM features. Autoencoders can be configured to detect abnormal patterns. The authors have used these abilities of the autoencoders to detect abnormalities in the HOFM features extracted from their novel spatio‐temporal regions of the video feeds. The autoencoders are trained on the HOFM features of the videos containing no abnormalities. The autoencoders are then fed with the HOFM features of the videos to be tested for abnormal activities, and these are detected based on the abilities of the autoencoders to reconstruct these features. The proposed method is tested on the standard abnormality detection datasets: UCSD Ped1, UCSD Ped2, Subway Entrance, Subway Exit, and UMN.https://doi.org/10.1049/iet-cvi.2018.5240standard abnormality detection datasetsautoencoder-based abnormal activity detectionparallelepiped spatio-temporal regionsurveillance camerasmanual monitoring systemautomatic abnormal activity detection |
| spellingShingle | Michael George Babita Roslind Jose Jimson Mathew Pranjali Kokare Autoencoder‐based abnormal activity detection using parallelepiped spatio‐temporal region IET Computer Vision standard abnormality detection datasets autoencoder-based abnormal activity detection parallelepiped spatio-temporal region surveillance cameras manual monitoring system automatic abnormal activity detection |
| title | Autoencoder‐based abnormal activity detection using parallelepiped spatio‐temporal region |
| title_full | Autoencoder‐based abnormal activity detection using parallelepiped spatio‐temporal region |
| title_fullStr | Autoencoder‐based abnormal activity detection using parallelepiped spatio‐temporal region |
| title_full_unstemmed | Autoencoder‐based abnormal activity detection using parallelepiped spatio‐temporal region |
| title_short | Autoencoder‐based abnormal activity detection using parallelepiped spatio‐temporal region |
| title_sort | autoencoder based abnormal activity detection using parallelepiped spatio temporal region |
| topic | standard abnormality detection datasets autoencoder-based abnormal activity detection parallelepiped spatio-temporal region surveillance cameras manual monitoring system automatic abnormal activity detection |
| url | https://doi.org/10.1049/iet-cvi.2018.5240 |
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