The use of the L-curve in the regularization of discrete ill-posed problems
SIAM Journal on Scientific Computing
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Special Section on Video Surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Variational Framework for Active and Adaptative Segmentation of Vector Valued Images
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
A Real-Time System for Monitoring of Cyclists and Pedestrians
VS '99 Proceedings of the Second IEEE Workshop on Visual Surveillance
Evaluation of global image thresholding for change detection
Pattern Recognition Letters
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Threshold dynamics for the piecewise constant Mumford-Shah functional
Journal of Computational Physics
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
IEEE Transactions on Image Processing
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
Level set-based bimodal segmentation with stationary global minimum
IEEE Transactions on Image Processing
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Most video surveillance systems require to manually set a motion detection sensitivity level to generate motion alarms. The performance of motion detection algorithms, embedded in closed circuit television (CCTV) camera and digital video recorder (DVR), usually depends upon the preselected motion sensitivity level, which is expected to work in all environmental conditions. Due to the preselected sensitivity level, false alarms and detection failures usually exist in video surveillance systems. The proposed motion detection model based upon variational energy provides a robust detection method at various illumination changes and noise levels of image sequences without tuning any parameter manually. We analyze the structure mathematically and demonstrate the effectiveness of the proposed model with numerous experiments in various environmental conditions. Due to the compact structure and efficiency of the proposed model, it could be implemented in a small embedded system.