Pfinder: Real-Time Tracking of the Human Body
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
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Improved Fast Gauss Transform and Efficient Kernel Density Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Emerging Topics in Computer Vision
Emerging Topics in Computer Vision
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
Probability density estimation from optimally condensed data samples
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fuzzy prediction based trajectory estimation
WSEAS TRANSACTIONS on SYSTEMS
Robust 2D moving object segmentation and tracking in video sequences
ICOSSE'06 Proceedings of the 5th WSEAS international conference on System science and simulation in engineering
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Background estimation and subtraction is a critical and time consuming step in moving object segmentation for video surveillance. Nonparametric kernel density estimation has been successfully used in modeling the background statistics, due to its capability to perform well without making any assumption about the form of the underlying distributions. To obtain real-time performance of the nonparametric estimator, we recently proposed an algorithm based on mean shift mode-tracking and a rough histogram test to fast discard foreground pixels from exact evaluation. In the present work, an improvement of the new algorithm is proposed, leading to faster background change tracking capability and more accurate background estimation.