Anomaly Detection Results

This page shows results found in the literature for anomalous event detection in crowd data sets. If you like to have your published results added in the following tables, please send an e-mail to Rensso Mora with the link (or the pdf) to your paper and the results to be reported. Up to now, we have tabulated the results for the following datasets.

  1. University of California, San Diego (UCSD), this data set contains two sequences Peds1 and Peds2
  2. UMN
  3. Subway Entrance-Exit

 

UCSD Dataset

MethodAUC Peds1Error Peds1AUC Peds2Error Peds2year
K-NN [7]0.92716.02012
Gaussian-KNN[13]0.92716.02015
Sparce-code[14]0.924315.02014
AMDN [8]0.92116.00.9017.02015
F-Matlab [11]0.91815.02013
Video Parsing[16]0.9118.00.9214.02011
OADC [6]0.910.9252015
CNN-Anom [9]0.8724.00.8824.42016
STT [10]0.8720.02015
Scan Statistic[15]0.870.942013
2D-HMM[17]0.85921.680.92811.672012
GPR [5]0.83823.72015
MDT-Temp [2]0.82522.90.76527.92014
Social-Attribute[12]0.78226.02015
HOMF [1]0.7133.60.8919.02015
Force Flow [4]0.68836.50.70235.02009
MPPCA [3]0.67435.60.7135.82009
HNC[18]10.010.02015
UCSD (Frame Level)

[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

 

MethodAUCERRyear
OADC-SA[9]0.99672015
CNN-anom[12]0.99632015
Multi_scale motion[3]0.9962013
MDT-Temp[7]0.9962.82014
Motion influence[5]0.9952013
Sparce code[8]0.9922014
Scan statistic[4]0.9912013
OACD[6]0.98872014
Social attribute[10]0.9862015
STT[11]0.9832015
K-NN[2]0.982012
Social Force[1]0.962009
UMN dataset – top ranked results (in %)

[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

 

MethodAUCERRyear
CNN-Anom*[2]0.927-0.9192015
GPR[2]0.92710.92015
MDT-temp*[1]0.897-0.90816.4-16.72014
Subway entrance-exit dataset – top ranked results (in %)

(*) 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.