Drone Video Anomaly Detection by Future Segmentation Prediction and Spatio- Temporal Relational Modeling
In traffic surveillance, accurate video anomaly detection is vital for public safety, yet environmental changes, occlusions, and visual obstructions pose significant challenges. In this research, we introduce DAD-FSM, an innovative drone-based video anomaly detection system that leverages a spatio-t...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10858152/ |
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| author | Ahmed Fakhry Janghoon Lee Jong Taek Lee |
| author_facet | Ahmed Fakhry Janghoon Lee Jong Taek Lee |
| author_sort | Ahmed Fakhry |
| collection | DOAJ |
| description | In traffic surveillance, accurate video anomaly detection is vital for public safety, yet environmental changes, occlusions, and visual obstructions pose significant challenges. In this research, we introduce DAD-FSM, an innovative drone-based video anomaly detection system that leverages a spatio-temporal relational cross-transformer to enhance the encoding of visual and temporal features for future segmentation. Additionally, we propose the motion-aware frame prediction loss function (MAFL) to improve the model’s representation and the background and foreground separation of moving objects. Our method achieves state-of-the-art (SOTA) AUC scores of 68.13% on the UIT-ADrone dataset and 73.5% mAUC on the Drone-Anomaly dataset, surpassing previous methods by 2.68% and 5.71% respectively. The approach is further validated on the CUHK Avenue dataset, underscoring its global applicability and effectiveness in diverse traffic scenarios. These results demonstrate the potential of our model for broad use in traffic surveillance applications. |
| format | Article |
| id | doaj-art-92377a3623c7416788b3c8ae88a6e610 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-92377a3623c7416788b3c8ae88a6e6102025-02-07T00:01:30ZengIEEEIEEE Access2169-35362025-01-0113223952240610.1109/ACCESS.2025.353662310858152Drone Video Anomaly Detection by Future Segmentation Prediction and Spatio- Temporal Relational ModelingAhmed Fakhry0https://orcid.org/0009-0006-7389-4449Janghoon Lee1https://orcid.org/0009-0003-1041-0898Jong Taek Lee2https://orcid.org/0000-0002-6962-3148School of Computer Science and Engineering, Kyungpook National University, Daegu, South KoreaSchool of Computer Science and Engineering, Kyungpook National University, Daegu, South KoreaSchool of Computer Science and Engineering, Kyungpook National University, Daegu, South KoreaIn traffic surveillance, accurate video anomaly detection is vital for public safety, yet environmental changes, occlusions, and visual obstructions pose significant challenges. In this research, we introduce DAD-FSM, an innovative drone-based video anomaly detection system that leverages a spatio-temporal relational cross-transformer to enhance the encoding of visual and temporal features for future segmentation. Additionally, we propose the motion-aware frame prediction loss function (MAFL) to improve the model’s representation and the background and foreground separation of moving objects. Our method achieves state-of-the-art (SOTA) AUC scores of 68.13% on the UIT-ADrone dataset and 73.5% mAUC on the Drone-Anomaly dataset, surpassing previous methods by 2.68% and 5.71% respectively. The approach is further validated on the CUHK Avenue dataset, underscoring its global applicability and effectiveness in diverse traffic scenarios. These results demonstrate the potential of our model for broad use in traffic surveillance applications.https://ieeexplore.ieee.org/document/10858152/Deep learningdrone anomaly detectionfuture segmentation predictiontraffic surveillance |
| spellingShingle | Ahmed Fakhry Janghoon Lee Jong Taek Lee Drone Video Anomaly Detection by Future Segmentation Prediction and Spatio- Temporal Relational Modeling IEEE Access Deep learning drone anomaly detection future segmentation prediction traffic surveillance |
| title | Drone Video Anomaly Detection by Future Segmentation Prediction and Spatio- Temporal Relational Modeling |
| title_full | Drone Video Anomaly Detection by Future Segmentation Prediction and Spatio- Temporal Relational Modeling |
| title_fullStr | Drone Video Anomaly Detection by Future Segmentation Prediction and Spatio- Temporal Relational Modeling |
| title_full_unstemmed | Drone Video Anomaly Detection by Future Segmentation Prediction and Spatio- Temporal Relational Modeling |
| title_short | Drone Video Anomaly Detection by Future Segmentation Prediction and Spatio- Temporal Relational Modeling |
| title_sort | drone video anomaly detection by future segmentation prediction and spatio temporal relational modeling |
| topic | Deep learning drone anomaly detection future segmentation prediction traffic surveillance |
| url | https://ieeexplore.ieee.org/document/10858152/ |
| work_keys_str_mv | AT ahmedfakhry dronevideoanomalydetectionbyfuturesegmentationpredictionandspatiotemporalrelationalmodeling AT janghoonlee dronevideoanomalydetectionbyfuturesegmentationpredictionandspatiotemporalrelationalmodeling AT jongtaeklee dronevideoanomalydetectionbyfuturesegmentationpredictionandspatiotemporalrelationalmodeling |