Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Neural networks for pattern recognition
Neural networks for pattern recognition
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection and Location of People in Video Images Using Adaptive Fusion of Color and Edge Information
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Evaluation of global image thresholding for change detection
Pattern Recognition Letters
Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Bayesian Object Detection in Dynamic Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
2D shape measurement of multiple moving objects by GMM background modeling and optical flow
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
| Hi-index | 0.00 |
Most statistical background subtraction techniques are based on the analysis of temporal color/intensity distributions. However, learning statistics on a series of time frames can be problematic, especially when no frames absent of moving objects are available or when the available memory isn’t sufficient to store the series of frames needed for learning. In this paper, we propose a framework that allows common statistical motion detection methods to use spatial statistics gathered on one frame instead of a series of frames as is usually the case. This simple and flexible framework is suitable for various applications including the ones with a mobile background such as when a tree is shaken by wind or when the camera jitters. Three statistical background subtraction methods have been adapted to the proposed framework and tested on different synthetic and real image sequences.