On the accuracy of binned kernel density estimators
Journal of Multivariate Analysis
Learning Patterns of Activity Using Real-Time Tracking
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
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Effective Gaussian Mixture Learning for Video Background Subtraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bi-Layer Segmentation of Binocular Stereo Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Background Subtraction on Distributions
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Learning complex background by multi-scale discriminative model
Pattern Recognition Letters
A comparative study of energy minimization methods for markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
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In video surveillance, it is still a difficult task to segment moving object accurately in complex scenes, since most widely used algorithms are background subtraction. We propose an online and unsupervised technique to find optimal segmentation in a Markov Random Field (MRF) framework. To improve the accuracy, color, locality, temporal coherence and spatial consistency are fused together in the framework. The models of color, locality and temporal coherence are learned online from complex scenes. A novel mixture of nonparametric regional model and parametric pixel-wise model is proposed to approximate the background color distribution. The foreground color distribution for every pixel is learned from neighboring pixels of previous frame. The locality distributions of background and foreground are approximated with the nonparametric model. The temporal coherence is modeled with a Markov chain. Experiments on challenging videos demonstrate the effectiveness of our algorithm.