Source: http://etds.lib.tku.edu.tw/etdservice/view_metadata?etdun=U0002-2308201312570800
Timestamp: 2019-04-24 12:13:54+00:00

Document:
英文摘要 In this paper a simple and effective crowd behavior normality method is proposed. Feature vector, so called HOSF (histogram of oriented social force), and consists of concatenating local histogram of oriented social force. A dictionary of codewords is trained to include typical HOSF. To detect whether an event is normal is accomplished by comparing how similar to the closest codeword via z-value. The proposed method includes the following characteristic: (1) the training is automatic instead of human labeling; (2) instead of object tracking, the method integrates particles and social force as feature descriptors which well adapted in both crowded or few people scenes; (3) z-score is used in measuring the normality of events. Due to computation simplicity, the normality detection could be real-time once the training is finished.
參考文獻 	T.B. Moeslund, A. Hilton and V. Kru‥ger, “A Survey of Advances in Vision-Based Human Motion Capture and Analysis,” Computer Vision and Image Understanding, vol. 104, no. 2, pp. 90–126, 2006.
	P.K. Turaga, R. Chellappa, V.S. Subrahmanian and O. Udrea, “Machine Recognition of Human Activities: A Survey,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 11, pp. 1473–1488, 2008.
	O.P. Popoola and K. Wang, “Video-Based Abnormal Human Behavior Recognition—A Review,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 42, pp. 6, 865–787, 2012.
	J. Yin, Q. Yang and J.J. Pan, “Sensor-Based Abnormal Human-Activity Detection,” IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 8, pp. 1082–1090, 2008.
	B. Zhao, L. Fei-Fei and E.P. Xing, “Online Detection of Unusual Events in Videos via Dynamic Sparse Coding,” Computer Vision and Pattern Recognition, pp. 3313–3320, 2011.
	R. Mehran, A. Oyama and M. Shah, “Abnormal Crowd Behavior Detection using Social Force Model,” Computer Vision and Pattern Recognition, pp. 935–942, 2009.
	G.R. Bradski, “Computer Vision Face Tracking for Use in a Perceptual User Interface,” IEEE Workshop on Applications of Computer Vision, pp. 214–219, 1998.
	N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” Computer Vision and Pattern Recognition, vol. 1, pp. 886–893, 2005.
	P. Viola, M.J. Jones and D. Snow, “Detecting Pedestrians using Patterns of Motion and Appearance,” International Journal of Computer Vision, vol. 63, no. 2, pp. 153–161, 2005.
	S. Saxena, F. Bremond, M. Thonnat and R. Ma, “Crowd Behavior Recognition for Video Surveillance,” International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 970–891, 2008.
	A. Adam, E. Rivlin, I. Shimshoni and D. Reinitz, “Robust Real Time Unusual Event Detection using Multiple Fixed-Location Monitors,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 3, pp. 555–560, 2008.
	L. Kratz and K. Nishino, “Anomaly Detection in Extremely Crowded Scenes using Spatio-Temporal Motion Pattern Models,” Computer Vision and Pattern Recognition, pp. 1446–1453, 2009.
	V. Mahadevan, W. Li, V. Bhalodia and N. Vasconcelos, “Anomaly Detection in Crowded Scenes,” Computer Vision and Pattern Recognition, pp. 1975–1981, 2010.
	V. Reddy, C. Sanderson and B.C. Lovell, “Improved Anomaly Detection in Crowded Scenes via Cell-based Analysis of Foreground Speed, Size and Texture,” IEEE Workshop on Computer Vision and Pattern Recognition, pp. 55–61, 2011.
	D. Helbing and P. Molnar, “Social Force Model for Pedestrian Dynamics,” Physical Review E, vol. 51, no. 5, pp. 4282–4286, 1995.
	Y. Zhang, L. Qin, H. Yao and Q. Huang, “Abnormal Crowd Behavior Detection Based on Social Attribute-Aware Force Model,” IEEE International Conference on Image Processing, pp. 2689 –2692, 2012.
	R. Raghavendra, A.D. Bue, M. Cristani and V. Murino, “Optimizing Interaction Force for Global Anomaly Detection in Crowded Scenes,” IEEE Workshop on Modeling, Simulation and Visual Analysis of Large Crowds, pp. 136–143, 2011.
	M. Luber, J.A. Stork, G.D. Tipaldi and K.O. Arras, “People Tracking with Human Motion Predictions from Social Forces,” International Conference on Robotics and Automation, pp. 464–469, 2010.
	H. Wang, M.M. Ullah, A. Klaser, I. Laptev and C, Schmid, “Evaluation of Local Spatio-Temporal Features for Action Recognition,” British Machine Vision Conference, pp. 1–11, 2009.
	L. Laptev and T. Lindeberg, “Space-Time Interest Points,” International Conference on Computer Vision, pp. 432–439, 2003.
	P. Dollar, V. Rabaud, G. Cottrell and S. Belongie, “Behavior Recognition via Sparse Spatio-Temporal Features,” IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 65–72, 2005.
	J.J. Gibson, “The Perception of the Visual World,” Houghton Mifflin, 1950.
	B.K.P. Horn and B.G. Schunck, “Determining Optical Flow,” Artificial Intelligence, vol. 17, pp. 185–203, 1981.
	B.D. Lucas and T. Kanade, “An Iterative Image Registration Technique with an Application to Stereo Vision,” International Joint Conference on Artificial Intelligence, vol. 2, pp. 121–130, 1981.
	J.-Y. Bouguet, “Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the Algorithm,” Intel Corporation, Microprocessor Research Labs, 2000.
	S.S. Beauchemin and J.L. Barron, “The Computation of Optical Flow,” ACM New York, USA, vol. 27, no. 3, pp. 433–466, 1995.
	C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.
	S.E. Lee, K. Min and T. Suh, “Accelerating Histograms of Oriented Gradients Descriptor Extraction for Pedestrian Recognition,” Computers and Electrical Engineering, vol. 39, issue 4, pp. 1043–1048, 2013.
	J.B. MacQueen, “Some Methods for Classification and Analysis of Multivariate Observations,” Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297, 1967.
	A.K. Jain, “Data Clustering: 50 Years beyond K-Means,” Pattern Recognition Letters, vol. 31, issue 8, pp. 651–666, 2010.
	D. Arthur and S. Vassilvitskii, “K-Means++: The Advantages of Careful Seeding,” Proceedings of the 18-th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035, 2007.
	“Unusual Crowd Activity Dataset of University of Minnesota,” available from http://mha.cs.umn.edu/Movies/Crowd-Activity-All.avi.

References: V. 
	V. 
 V. 
	V. 
 V. 
 V. 
 V.