Advanced video anomaly detection using 2D CNN and stacked LSTM with deep active learning-based model
Around the world, the video surveillance system has gained wide acceptance and astonishing growth due to its broad applications. The surveillance system has become a paramount tool and benchmark for analyzing the harmony and safety of society. Anomaly detection and its associated applications play...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-06-01
|
| Series: | Kuwait Journal of Science |
| Online Access: | https://journalskuwait.org/kjs/index.php/KJS/article/view/19159 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850231205956419584 |
|---|---|
| author | ANOOPA S Dr Salim A Dr Nadera Beevi S |
| author_facet | ANOOPA S Dr Salim A Dr Nadera Beevi S |
| author_sort | ANOOPA S |
| collection | DOAJ |
| description |
Around the world, the video surveillance system has gained wide acceptance and astonishing growth due to its
broad applications. The surveillance system has become a paramount tool and benchmark for analyzing the harmony and safety of society. Anomaly detection and its associated applications play a key role in the integrity of
the system. The aim of anomaly detection is to find rare and sparse occurrences of events from videos. Developing an accurate and time-efficient system is still remains challenging due to the dynamic nature of anomalies. An
active learning-based end-to-end system with full use of both spatial and temporal features from the input videos
is proposed. The model combines the use of 2DCNN and Stacked LSTM to extract frame-level features through
an improved anisotropic Gunnar Farneback Optical Flow algorithm. The system is evaluated on the benchmarked
datasets namely UCSD Ped1 UCSD Ped2 and achieves an AUC of 95% and 94% respectively. The experimental
results indicate that the proposed method is superior to state-of-the-art algorithms.
|
| format | Article |
| id | doaj-art-7cd2d50feeaa4b92a4bbf12a51a6ebc6 |
| institution | OA Journals |
| issn | 2307-4108 2307-4116 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Kuwait Journal of Science |
| spelling | doaj-art-7cd2d50feeaa4b92a4bbf12a51a6ebc62025-08-20T02:03:36ZengElsevierKuwait Journal of Science2307-41082307-41162022-06-0110.48129/kjs.splml.19159Advanced video anomaly detection using 2D CNN and stacked LSTM with deep active learning-based modelANOOPA S0Dr Salim ADr Nadera Beevi SAPJ Abdul Kalam Technological University (KERALA) Around the world, the video surveillance system has gained wide acceptance and astonishing growth due to its broad applications. The surveillance system has become a paramount tool and benchmark for analyzing the harmony and safety of society. Anomaly detection and its associated applications play a key role in the integrity of the system. The aim of anomaly detection is to find rare and sparse occurrences of events from videos. Developing an accurate and time-efficient system is still remains challenging due to the dynamic nature of anomalies. An active learning-based end-to-end system with full use of both spatial and temporal features from the input videos is proposed. The model combines the use of 2DCNN and Stacked LSTM to extract frame-level features through an improved anisotropic Gunnar Farneback Optical Flow algorithm. The system is evaluated on the benchmarked datasets namely UCSD Ped1 UCSD Ped2 and achieves an AUC of 95% and 94% respectively. The experimental results indicate that the proposed method is superior to state-of-the-art algorithms. https://journalskuwait.org/kjs/index.php/KJS/article/view/19159 |
| spellingShingle | ANOOPA S Dr Salim A Dr Nadera Beevi S Advanced video anomaly detection using 2D CNN and stacked LSTM with deep active learning-based model Kuwait Journal of Science |
| title | Advanced video anomaly detection using 2D CNN and stacked LSTM with deep active learning-based model |
| title_full | Advanced video anomaly detection using 2D CNN and stacked LSTM with deep active learning-based model |
| title_fullStr | Advanced video anomaly detection using 2D CNN and stacked LSTM with deep active learning-based model |
| title_full_unstemmed | Advanced video anomaly detection using 2D CNN and stacked LSTM with deep active learning-based model |
| title_short | Advanced video anomaly detection using 2D CNN and stacked LSTM with deep active learning-based model |
| title_sort | advanced video anomaly detection using 2d cnn and stacked lstm with deep active learning based model |
| url | https://journalskuwait.org/kjs/index.php/KJS/article/view/19159 |
| work_keys_str_mv | AT anoopas advancedvideoanomalydetectionusing2dcnnandstackedlstmwithdeepactivelearningbasedmodel AT drsalima advancedvideoanomalydetectionusing2dcnnandstackedlstmwithdeepactivelearningbasedmodel AT drnaderabeevis advancedvideoanomalydetectionusing2dcnnandstackedlstmwithdeepactivelearningbasedmodel |