The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Learning Temporal Regularity in Video Sequences
Abstract
Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene. We approach this problem by learning a generative model for regular motion patterns (termed as regularity) using multiple sources with very limited supervision. Specifically, we propose two methods that are built upon the autoencoders for their ability to work with little to no supervision. We first leverage the conventional handcrafted spatio-temporal local features and learn a fully connected autoencoder on them. Second, we build a fully convolutional feed-forward autoencoder to learn both the local features and the classifiers as an end-to-end learning framework. Our model can capture the regularities from multiple datasets. We evaluate our methods in both qualitative and quantitative ways - showing the learned regularity of videos in various aspects and demonstrating competitive performance on anomaly detection datasets as an application.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
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Large-Scale Video Classification with Convolutional Neural Networks
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Deep visual-semantic alignments for generating image descriptions
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Cited by
Gong, D., Liu, L., Le, V., Saha, B., Mansour, M. R., Venkatesh, S., & Van Den Hengel, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00179 · Cited by 1,930
Deep Learning for Anomaly Detection: A Survey
Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240
Deep learning modelling techniques: Current progress, applications, advantages, and challenges
Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10466-8 · Cited by 1,079
A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework
Luo, W., Liu, W., & Gao, S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.45 · Cited by 895
Latent Space Autoregression for Novelty Detection
Abati, D., Porrello, A., Calderara, S., & Cucchiara, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00057 · Cited by 508
Metadata
- Authors
- Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A. K., & Davis, L. S.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 733-742
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2341058432
- Cited by
- 1,397
- References
- 11
- FWCI
- 27.77
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A. K., & Davis, L. S. (2016). Learning Temporal Regularity in Video Sequences. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.86
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