The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence
Open accessAbstract
Anomaly detection in surveillance videos is currently a challenge because of the diversity of possible events. We propose a deep convolutional neural network (CNN) that addresses this problem by learning a correspondence between common object appearances (e.g. pedestrian, background, tree, etc.) and their associated motions. Our model is designed as a combination of a reconstruction network and an image translation model that share the same encoder. The former sub-network determines the most significant structures that appear in video frames and the latter one attempts to associate motion templates to such structures. The training stage is performed using only videos of normal events and the model is then capable to estimate frame-level scores for an unknown input. The experiments on 6 benchmark datasets demonstrate the competitive performance of the proposed approach with respect to state-of-the-art methods.
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Going deeper with convolutions
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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
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
An explainable and efficient deep learning framework for video anomaly detection
Wu, C., Shao, S., Tunc, C., Satam, P., & Hariri, S. (2022) · Cluster Computing · doi:10.1007/s10586-021-03439-5 · Cited by 88
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Metadata
- Authors
- Nguyen, T. N., & Meunier, J.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 1273-1283
- OpenAlex
- W2968225932
- Cited by
- 459
- References
- 48
- FWCI
- 20.53
- Type
- conference paper
- Language
- en
- Open access
- green
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