The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Sparse reconstruction cost for abnormal event detection
Abstract
We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given an over-complete normal basis set (e.g., an image sequence or a collection of local spatio-temporal patches), we introduce the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. To condense the size of the dictionary, a novel dictionary selection method is designed with sparsity consistency constraint. By introducing the prior weight of each basis during sparse reconstruction, the proposed SRC is more robust compared to other outlier detection criteria. Our method provides a unified solution to detect both local abnormal events (LAE) and global abnormal events (GAE). We further extend it to support online abnormal event detection by updating the dictionary incrementally. Experiments on three benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our algorithm.
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Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Robust Face Recognition via Sparse Representation
Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Ma, Y. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.79 · Cited by 9,388
Model Selection and Estimation in Regression with Grouped Variables
Yuan, M., & Lin, Y. (2006) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00532.x · Cited by 7,261
Adaptive background mixture models for real-time tracking
Stauffer, C., & Grimson, W. E. L. (2003) · Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) · doi:10.1109/cvpr.1999.784637 · Cited by 6,923
Social force model for pedestrian dynamics
Helbing, D., & Molnár, P. (1995) · Physical Review E · doi:10.1103/physreve.51.4282 · Cited by 6,845
Tensor Completion for Estimating Missing Values in Visual Data
Liu, J., Musialski, P., Wonka, P., & Ye, J. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.39 · Cited by 2,063
Self-citationAbnormal crowd behavior detection using social force model
Mehran, R., Oyama, A., & Shah, M. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206641 · Cited by 1,672
Anomaly detection in crowded scenes
Mahadevan, V., Li, W., Bhalodia, V., & Vasconcelos, N. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539872 · Cited by 1,512
Avidan, S. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.35 · Cited by 1,112
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
Adam, A., Rivlin, E., Shimshoni, I., & Reinitz, D. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70825 · Cited by 918
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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
Adversarially Learned One-Class Classifier for Novelty Detection
Sabokrou, M., Khalooei, M., Fathy, M., & Adeli, E. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00356 · Cited by 765
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Metadata
- Authors
- Cong, Y., Yuan, J., & Liu, J.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 3449-3456
- OpenAlex
- W2012931101
- Cited by
- 801
- References
- 29
- FWCI
- 29.14
- Type
- conference paper
- Language
- en
- Open access
- closed
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