The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos
Open accessAbstract
Appearance features have been widely used in video anomaly detection even though they contain complex entangled factors. We propose a new method to model the normal patterns of human movements in surveillance video for anomaly detection using dynamic skeleton features. We decompose the skeletal movements into two sub-components: global body movement and local body posture. We model the dynamics and interaction of the coupled features in our novel Message-Passing Encoder-Decoder Recurrent Network. We observed that the decoupled features collaboratively interact in our spatio-temporal model to accurately identify human-related irregular events from surveillance video sequences. Compared to traditional appearance-based models, our method achieves superior outlier detection performance. Our model also offers “open-box” examination and decision explanation made possible by the semantically understandable features and a network architecture supporting interpretability.
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RMPE: Regional Multi-person Pose Estimation
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Future Frame Prediction for Anomaly Detection - A New Baseline
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TransCNN: Hybrid CNN and transformer mechanism for surveillance anomaly detection
Ullah, W., Hussain, T., Ullah, F. U. M., Lee, M. Y., & Baik, S. W. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106173 · Cited by 128
Deep Video Anomaly Detection: Opportunities and Challenges
Ren, J., Xia, F., Liu, Y., & Lee, I. (2021) · 2021 International Conference on Data Mining Workshops (ICDMW) · doi:10.1109/icdmw53433.2021.00125 · Cited by 84
TDS-Net: Transformer enhanced dual-stream network for video Anomaly Detection
Hussain, A., Ullah, W., Khan, N., Khan, Z. A., Kim, M. J., & Baik, S. W. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124846 · Cited by 47
Li, N., Chang, F., & Liu, C. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.12.023 · Cited by 36
MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection
Ding, H., Lou, S., Ye, H., & Chen, Y. (2025) · Applied Sciences · doi:10.3390/app15126773 · Cited by 14
Metadata
- Authors
- Morais, R., Le, V., Tran, T., Saha, B., Mansour, M., & Venkatesh, S.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 11988-11996
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2921906393
- Cited by
- 341
- References
- 27
- FWCI
- 14.82
- Type
- conference paper
- Language
- en
- Open access
- green
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- Morais, R., Le, V., Tran, T., Saha, B., Mansour, M., & Venkatesh, S. (2019). Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.01227
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