The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction
Abstract
In this paper, we propose HF2-VAD, a Hybrid framework that integrates Flow reconstruction and Frame prediction seamlessly to handle Video Anomaly Detection. Firstly, we design the network of ML-MemAE-SC (Multi-Level Memory modules in an Autoencoder with Skip Connections) to memorize normal patterns for optical flow reconstruction so that abnormal events can be sensitively identified with larger flow reconstruction errors. More importantly, conditioned on the reconstructed flows, we then employ a Conditional Variational Autoencoder (CVAE), which captures the high correlation between video frame and optical flow, to predict the next frame given several previous frames. By CVAE, the quality of flow reconstruction essentially influences that of frame prediction. Therefore, poorly reconstructed optical flows of abnormal events further deteriorate the quality of the final predicted future frame, making the anomalies more detectable. Experimental results demonstrate the effectiveness of the proposed method. Code is available at https://github.com/LiUzHiAn/hf2vad.
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Cited by
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
Ristea, N.-C., Madan, N., Ionescu, R. T., Nasrollahi, K., Khan, F. S., Moeslund, T. B., & Shah, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01321 · Cited by 315
Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey
Duong, H.-T., Le, V.-T., & Hoang, V. T. (2023) · Sensors · doi:10.3390/s23115024 · Cited by 178
Weakly Supervised Video Anomaly Detection via Transformer-Enabled Temporal Relation Learning
Zhang, D., Huang, C., Liu, C., & Xu, Y. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3175092 · Cited by 58
SwinAnomaly: Real-Time Video Anomaly Detection Using Video Swin Transformer and SORT
Bajgoti, A., Gupta, R., Balaji, P., Dwivedi, R., Siwach, M., & Gupta, D. (2023) · IEEE Access · doi:10.1109/access.2023.3321801 · Cited by 38
Nazir, A., Mitra, R., Sulieman, H., & Kamalov, F. (2023) · Sensors · doi:10.3390/s23135811 · Cited by 30
Metadata
- Authors
- Liu, Z., Nie, Y., Long, C., Zhang, Q., & Li, G.
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 13568-13577
- OpenAlex
- W3202590754
- Cited by
- 328
- References
- 46
- FWCI
- 25.42
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Liu, Z., Nie, Y., Long, C., Zhang, Q., & Li, G. (2021). A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv48922.2021.01333
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