The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Online detection of unusual events in videos via dynamic sparse coding
Open accessAbstract
Real-time unusual event detection in video stream has been a difficult challenge due to the lack of sufficient training information, volatility of the definitions for both normality and abnormality, time constraints, and statistical limitation of the fitness of any parametric models. We propose a fully unsupervised dynamic sparse coding approach for detecting unusual events in videos based on online sparse re-constructibility of query signals from an atomically learned event dictionary, which forms a sparse coding bases. Based on an intuition that usual events in a video are more likely to be reconstructible from an event dictionary, whereas unusual events are not, our algorithm employs a principled convex optimization formulation that allows both a sparse reconstruction code, and an online dictionary to be jointly inferred and updated. Our algorithm is completely un-supervised, making no prior assumptions of what unusual events may look like and the settings of the cameras. The fact that the bases dictionary is updated in an online fashion as the algorithm observes more data, avoids any issues with concept drift. Experimental results on hours of real world surveillance video and several Youtube videos show that the proposed algorithm could reliably locate the unusual events in the video sequence, outperforming the current state-of-the-art methods.
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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
Future Frame Prediction for Anomaly Detection - A New Baseline
Liu, W., Luo, W., Lian, D., & Gao, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00684 · Cited by 1,530
Anomaly Detection and Localization in Crowded Scenes
Li, W., Mahadevan, V., & Vasconcelos, N. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.111 · Cited by 1,017
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
- Zhao, B., Fei-Fei, L., & Xing, E. P.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 3313-3320
- OpenAlex
- W2021659075
- Cited by
- 532
- References
- 23
- FWCI
- 19.85
- Type
- conference paper
- Language
- en
- Open access
- green
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