Resultados de Detecção de Anomalia
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Esta página mostra resultados encontrados na literatura para detecção de eventos anômalos em datasets de multidões. Se você gostaria de ter seus resultados publicados adicionados nas tabelas a seguir, envie um e-mail para Rensso Mora com o link (ou o pdf) para o seu trabalho e os resultados a serem relatados. Até agora, tabulamos os resultados para os seguintes conjuntos de dados.
- University of California, San Diego (UCSD), this data set contains two sequences Peds1 and Peds2
- UMN
- Subway Entrance-Exit
Dataset UCSD
| Método | AUC Peds1 | Error Peds1 | AUC Peds2 | Error Peds2 | Ano |
|---|---|---|---|---|---|
| K-NN [7] | 0.927 | 16.0 | – | – | 2012 |
| Gaussian-KNN[13] | 0.927 | 16.0 | – | – | 2015 |
| Sparce-code[14] | 0.9243 | 15.0 | – | – | 2014 |
| AMDN [8] | 0.921 | 16.0 | 0.90 | 17.0 | 2015 |
| F-Matlab [11] | 0.918 | 15.0 | – | – | 2013 |
| Video Parsing[16] | 0.91 | 18.0 | 0.92 | 14.0 | 2011 |
| OADC [6] | 0.91 | – | 0.925 | – | 2015 |
| CNN-Anom [9] | 0.87 | 24.0 | 0.88 | 24.4 | 2016 |
| STT [10] | 0.87 | 20.0 | – | – | 2015 |
| Scan Statistic[15] | 0.87 | – | 0.94 | – | 2013 |
| 2D-HMM[17] | 0.859 | 21.68 | 0.928 | 11.67 | 2012 |
| GPR [5] | 0.838 | 23.7 | – | – | 2015 |
| MDT-Temp [2] | 0.825 | 22.9 | 0.765 | 27.9 | 2014 |
| Social-Attribute[12] | 0.782 | 26.0 | – | – | 2015 |
| HOMF [1] | 0.71 | 33.6 | 0.89 | 19.0 | 2015 |
| Force Flow [4] | 0.688 | 36.5 | 0.702 | 35.0 | 2009 |
| MPPCA [3] | 0.674 | 35.6 | 0.71 | 35.8 | 2009 |
| HNC[18] | – | 10.0 | – | 10.0 | 2015 |
[1] Rensso Victor Hugo Mora Colque, Carlos Caetano, William Robson Schwartz, “Histograms of Optical Flow Orientation and Magnitude to Detect Anomalous Events in Videos”, in SIBGRAPI, 2015.
[2] W. Li, V. Mahadevan, N. Vasconcelos, “Anomaly detection and localization in crowded scenes,” in IEEE Transactions on, 2014.
[3] J. Kim, K. Grauman , “Observe locally, infer globally: A spacetime mrf for detecting abnormal activities with incremental updates,” in CVPR, 2009.
[4] R. Mehran, A. Oyama, M. Shah, “Abnormal crowd behavior detection using social force model,” in CVPR, 2009.
[5] Kai-Wen Cheng, Yie-Tarng Chen, Wen-Hsien Fang “Video Anomaly Detection and Localization Using Hierarchical Feature Representation and Gaussian Process Regression,” in CVPR, 2015.
[6] Y. Yuan and J. Fang and Q. Wang “Online Anomaly Detection in Crowd Scenes via Structure Analysis,” in IEEE Transactions On Cybernetics, 2015.
[7] V. Saligrama, Z. Chen “Video anomaly detection based on local statistical aggregates,” in CVPR, 2012.
[8] Xu, D., Ricci, E., Yan, Y., Song, J., Sebe, N., Kessler, F.B. “Learning deep representations of appearance and motion for anomalous event detection ,” in BMVC, 2015.
[9] Zhou, S., Shen, W., Zeng, D., Fang, M., Wei, Y., & Zhang, Z. “Spatial-temporal convolutional neural networks for anomaly detection and localization in crowded scenes ,” in Signal Processing: Image Communication, 2016.
[10] Zhang, Y., Lu, H., Zhang, L., Ruan, X., & Sakai, S.“Video anomaly detection based on locality sensitive hashing filters,” in Pattern Recognition, 2015.
[11] Lu, C., Shi, J., & Jia, J. “Abnormal Event Detection at 150 FPS in MATLAB,” in International Conference on Computer Vision, 2013.
[12] Zhang, Y., Qin, L., Ji, R., Yao, H., & Huang, Q. “Social Attribute-Aware Force Model: Exploiting Richness of Interaction for Abnormal Crowd Detection,” in IEEE Transactions on Circuits and Systems for Video Technology, 2015.
[13] Cheng, K., Chen, Y., & Fang, W. “ Gaussian Process Regression-Based Video Anomaly Detection and Localization With Hierarchical Feature Representation,” in IEEE Transactions on Image Processing, 2015.
[14] Liu, Y., Li, Y., & Ji, X. “ Abnormal Event Detection in Nature Settings,” in International Journal of Signal Processing, Image Processing and Pattern Recognition, 2014.
[15] Hu, Y., Zhang, Y., & Davis, L. S. “ Unsupervised abnormal crowd activity detection using semiparametric scan statistic,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2013.
[16] Antić, B., & Ommer, B. “ Video parsing for abnormality detection,” in Proceedings of the IEEE International Conference on Computer Vision, 2011.
[17] Nallaivarothayan, H., Ryan, D., Denman, S., Sridharan, S., & Fookes, C. “ Anomalous Event Detection Using a Semi-Two Dimensional Hidden Markov Model ,” in In Digital Image Computing Techniques and Applications (DICTA), 2012.
[18] Xiao, T., Zhang, C., & Zha, H. “Learning to detect anomalies in surveillance video ,” in IEEE Signal Processing Letters, 2015.
UMN Dataset
| Método | AUC | ERR | Ano |
|---|---|---|---|
| OADC-SA[9] | 0.9967 | – | 2015 |
| CNN-anom[12] | 0.9963 | – | 2015 |
| Multi_scale motion[3] | 0.996 | – | 2013 |
| MDT-Temp[7] | 0.996 | 2.8 | 2014 |
| Motion influence[5] | 0.995 | – | 2013 |
| Sparce code[8] | 0.992 | – | 2014 |
| Scan statistic[4] | 0.991 | – | 2013 |
| OACD[6] | 0.9887 | – | 2014 |
| Social attribute[10] | 0.986 | – | 2015 |
| STT[11] | 0.983 | – | 2015 |
| K-NN[2] | 0.98 | – | 2012 |
| Social Force[1] | 0.96 | – | 2009 |
[1] Mehran, R., Oyama, A., & Shah, M. “ Abnormal crowd behavior detection using social force model,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009.
[2] V. Saligrama, Z. Chen “Video anomaly detection based on local statistical aggregates,” in CVPR, 2012.
[3] Du, D., Qi, H., Huang, Q., Zeng, W., & Zhang, C. “ Abnormal event detection in crowded scenes based on Structural Multi-scale Motion Interrelated Patterns,” in Proceedings – IEEE International Conference on Multimedia and Expo, 2013.
[4] Hu, Y., Zhang, Y., & Davis, L. S. “ Unsupervised abnormal crowd activity detection using semiparametric scan statistic,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2013.
[5] Lee, D. G., Suk, H. Il, & Lee, S. W. “ Crowd behavior representation using motion influence matrix for anomaly detection,” in 2nd IAPR Asian Conference on Pattern Recognition, 2013.
[6] Liu, Y., Li, Y., & Ji, X. “ Abnormal Event Detection in Nature Settings,” in International Journal of Signal Processing, Image Processing and Pattern Recognition, 2014.
[7] W. Li, V. Mahadevan, N. Vasconcelos, “Anomaly detection and localization in crowded scenes,” in IEEE Transactions on, 2014.
[8] Liu, Y., Li, Y., & Ji, X. “ Abnormal Event Detection in Nature Settings,” in International Journal of Signal Processing, Image Processing and Pattern Recognition, 2014.
[9] Yuan, Y., Fang, J., & Wang, Q. “Online anomaly detection in crowd scenes via structure analysis ,” in IEEE Transactions on Cybernetics, 2015.
[10] Zhang, Y., Qin, L., Ji, R., Yao, H., & Huang, Q. “Social Attribute-Aware Force Model: Exploiting Richness of Interaction for Abnormal Crowd Detection.,” in IEEE Transactions on Circuits and Systems for Video Technology, 2015.
[11] Zhang, Y., Lu, H., Zhang, L., Ruan, X., & Sakai, S.“Video anomaly detection based on locality sensitive hashing filters,” in Pattern Recognition, 2015.
[12] Zhou, S., Shen, W., Zeng, D., Fang, M., Wei, Y., & Zhang, Z. “Spatial-temporal convolutional neural networks for anomaly detection and localization in crowded scenes ,” in Signal Processing: Image Communication, 2016.
Subway Entrance-Exit Dataset
| Método | AUC | ERR | Ano |
|---|---|---|---|
| CNN-Anom*[2] | 0.927-0.919 | – | 2015 |
| GPR[2] | 0.927 | 10.9 | 2015 |
| MDT-temp*[1] | 0.897-0.908 | 16.4-16.7 | 2014 |
(*) Some results are presented for entrance and exit sequences respectively.
[1] W. Li, V. Mahadevan, N. Vasconcelos, “Anomaly detection and localization in crowded scenes,” in IEEE Transactions on, 2014.
[2] Kai-Wen Cheng, Yie-Tarng Chen, Wen-Hsien Fang “Video Anomaly Detection and Localization Using Hierarchical Feature Representation and Gaussian Process Regression,” in CVPR, 2015.
[3] Zhou, S., Shen, W., Zeng, D., Fang, M., Wei, Y., & Zhang, Z. “Spatial-temporal convolutional neural networks for anomaly detection and localization in crowded scenes ,” in Signal Processing: Image Communication, 2016.
