Digital Image Processing
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Counting People in Crowds with a Real-Time Network of Simple Image Sensors
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Counting Crowded Moving Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Viewpoint Invariant Approach for Crowd Counting
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
A Statistical Method for People Counting in Crowded Environments
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast and Robust People Counting Method in Video Surveillance
CIS '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security
Estimating pedestrian counts in groups
Computer Vision and Image Understanding
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene segmentation based on IPCA for visual surveillance
Neurocomputing
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Spatio-temporal event detection using dynamic conditional random fields
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Abnormal crowd motion analysis
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Tracking multiple humans in crowded environment
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Video-Based Detection of Abnormal Behavior in the Examination Room
IFITA '10 Proceedings of the 2010 International Forum on Information Technology and Applications - Volume 03
A block-based model for monitoring of human activity
Neurocomputing
Max-Min Distance Analysis by Using Sequential SDP Relaxation for Dimension Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of number of people in crowded scenes using perspective transformation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Non-Negative Patch Alignment Framework
IEEE Transactions on Neural Networks
Pedestrian analysis and counting system with videos
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
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Abnormal crowd behavior detection plays an important role in surveillance applications. We propose a camera parameter independent and perspective distortion invariant approach to detect two types of abnormal crowd behavior. The two typical abnormal activities are people gathering and running. Since people counting is necessary for detecting the abnormal crowd behavior, we present an potential energy-based model to estimate the number of people in public scenes. Building histograms on the X- and Y-axes, respectively, we can obtain probability distribution of the foreground object and then define crowd entropy. We define the Crowd Distribution Index by combining the people counting results with crowd entropy to represent the spatial distribution of crowd. We set a threshold on Crowd Distribution Index to detect people gathering. To detect people running, the kinetic energy is determined by computation of optical flow and Crowd Distribution Index. With a threshold, kinetic energy can be used to detect people running. To test the performance of our algorithm, videos of different scenes and different crowd densities are used in the experiments. Without camera calibration and training data, our method can robustly detect abnormal behaviors with low computation load.