On the accuracy of binned kernel density estimators
Journal of Multivariate Analysis
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
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multivariate locally adaptive density estimation
Computational Statistics & Data Analysis
An HMM-Based Segmentation Method for Traffic Monitoring Movies
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
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Feature Selection for Background Subtraction
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Robust Foreground Detection In Video Using Pixel Layers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
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
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
Spatially correlated background subtraction, based on adaptive background maintenance
Journal of Visual Communication and Image Representation
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We present a nonparametric background subtraction method that uses the local spatial co-occurrence correlations between neighboring pixels to robustly and efficiently detect moving objects in dynamic scenes We first represent each pixel as a joint feature vector consisting of its spatial coordinates and appearance properties (e.g., intensities, color, edges, or gradients) This joint feature vector naturally fuses spatial and appearance features to simultaneously consider meaningful correlation between neighboring pixels and pixels' appearance changes, which are very important for dynamic background modeling Then, each pixel's background model is modeled via an adaptive binned kernel estimation, which is updated by the neighboring pixels' feature vectors in a local rectangle region around the pixel The adaptive binned kernel estimation is adopted due to it is computationally inexpensive and does not need any assumptions about the underlying distributions Qualitative and quantitative experimental results on challenging video sequences demonstrate the robustness of the proposed method.