Object Tracking with Bayesian Estimation of Dynamic Layer Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Tracking Multiple Humans in Complex Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective Gaussian Mixture Learning for Video Background Subtraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence Review
International Journal of Computer Vision
An Improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
A survey on behavior analysis in video surveillance for homeland security applications
AIPR '08 Proceedings of the 2008 37th IEEE Applied Imagery Pattern Recognition Workshop
Independent component analysis-based background subtraction for indoor surveillance
IEEE Transactions on Image Processing
Improved background mixture models for video surveillance applications
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Unsupervised intrusion detection using color images
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Survey on classifying human actions through visual sensors
Artificial Intelligence Review
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
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Background subtraction is usually one of the first steps carried out in motion detection using static video cameras. This paper presents a new fast model for background subtraction that processes only some pixels of each image. This model achieves a significant reduction in computation time that can be used for subsequent image analysis. Some regions of interest (ROI) are located where movement can start. If no movement is present in the image, only pixels of these ROIs are processed. Once a moving object is detected, a new ROI that follows it is created. Thus, motion detection and parameter updates are executed only in the relevant areas instead of in the whole image. The proposed model has three main advantages: the computational time can be reduced drastically, motion detection performance is improved, and it can be combined with most of the existing background subtraction techniques. These features make it specially suitable for security applications.