Tucker tensor decomposition‐based tracking and Gaussian mixture model for anomaly localisation and detection in surveillance videos
The anomaly detection and localisation (ADL) gains remarkable interest as dealing with the complex surveillance videos for detecting the abnormal behaviour is tedious. The human effort in monitoring and classifying the abnormal object is inaccurate and time‐consuming; therefore, the method is propos...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-09-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2017.0469 |
| _version_ | 1797684828050554880 |
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| author | Avinash Ratre Vinod Pankajakshan |
| author_facet | Avinash Ratre Vinod Pankajakshan |
| author_sort | Avinash Ratre |
| collection | DOAJ |
| description | The anomaly detection and localisation (ADL) gains remarkable interest as dealing with the complex surveillance videos for detecting the abnormal behaviour is tedious. The human effort in monitoring and classifying the abnormal object is inaccurate and time‐consuming; therefore, the method is proposed using the Tucker tensor decomposition (TTD) and classification of the objects using Gaussian mixture model (GMM). Initially, the object is detected in the frames for easy recognition using simple background subtraction. The TTD decomposes the tensor as core tensor and factor matrices and the two decomposed tensors are compared using the cosine similarity measure that determines the location of the object in the frame. Finally, the features including shape and speed of the object are extracted that is used for classification using the GMM that follows the maximum posterior probability principle to detect and locate the anomaly in the video. The experimentation for anomaly detection proves that the proposed TTD and TTD‐GMM method attains a higher rate of multiple object tracking precision, accuracy, sensitivity, and specificity at 0.96375, 0.975, 1, and 1, respectively. |
| first_indexed | 2024-03-12T00:35:22Z |
| format | Article |
| id | doaj.art-a4c3afa0d7814df486330bf7c17051ec |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:35:22Z |
| publishDate | 2018-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-a4c3afa0d7814df486330bf7c17051ec2023-09-15T09:46:19ZengWileyIET Computer Vision1751-96321751-96402018-09-0112693394010.1049/iet-cvi.2017.0469Tucker tensor decomposition‐based tracking and Gaussian mixture model for anomaly localisation and detection in surveillance videosAvinash Ratre0Vinod Pankajakshan1Department of Electronics and Communication EngineeringIndian Institute of TechnologyRoorkee(Uttarakhand)247667IndiaDepartment of Electronics and Communication EngineeringIndian Institute of TechnologyRoorkee(Uttarakhand)247667IndiaThe anomaly detection and localisation (ADL) gains remarkable interest as dealing with the complex surveillance videos for detecting the abnormal behaviour is tedious. The human effort in monitoring and classifying the abnormal object is inaccurate and time‐consuming; therefore, the method is proposed using the Tucker tensor decomposition (TTD) and classification of the objects using Gaussian mixture model (GMM). Initially, the object is detected in the frames for easy recognition using simple background subtraction. The TTD decomposes the tensor as core tensor and factor matrices and the two decomposed tensors are compared using the cosine similarity measure that determines the location of the object in the frame. Finally, the features including shape and speed of the object are extracted that is used for classification using the GMM that follows the maximum posterior probability principle to detect and locate the anomaly in the video. The experimentation for anomaly detection proves that the proposed TTD and TTD‐GMM method attains a higher rate of multiple object tracking precision, accuracy, sensitivity, and specificity at 0.96375, 0.975, 1, and 1, respectively.https://doi.org/10.1049/iet-cvi.2017.0469Tucker tensor decomposition-based trackingGaussian mixture modelanomaly detection and localisationADLcomplex surveillance videosabnormal behaviour detection |
| spellingShingle | Avinash Ratre Vinod Pankajakshan Tucker tensor decomposition‐based tracking and Gaussian mixture model for anomaly localisation and detection in surveillance videos IET Computer Vision Tucker tensor decomposition-based tracking Gaussian mixture model anomaly detection and localisation ADL complex surveillance videos abnormal behaviour detection |
| title | Tucker tensor decomposition‐based tracking and Gaussian mixture model for anomaly localisation and detection in surveillance videos |
| title_full | Tucker tensor decomposition‐based tracking and Gaussian mixture model for anomaly localisation and detection in surveillance videos |
| title_fullStr | Tucker tensor decomposition‐based tracking and Gaussian mixture model for anomaly localisation and detection in surveillance videos |
| title_full_unstemmed | Tucker tensor decomposition‐based tracking and Gaussian mixture model for anomaly localisation and detection in surveillance videos |
| title_short | Tucker tensor decomposition‐based tracking and Gaussian mixture model for anomaly localisation and detection in surveillance videos |
| title_sort | tucker tensor decomposition based tracking and gaussian mixture model for anomaly localisation and detection in surveillance videos |
| topic | Tucker tensor decomposition-based tracking Gaussian mixture model anomaly detection and localisation ADL complex surveillance videos abnormal behaviour detection |
| url | https://doi.org/10.1049/iet-cvi.2017.0469 |
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