Motion-based background modeling for foreground segmentation
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
Linguistic summarization of video for fall detection using voxel person and fuzzy logic
Computer Vision and Image Understanding
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Learning complex background by multi-scale discriminative model
Pattern Recognition Letters
Object motion detection using information theoretic spatio-temporal saliency
Pattern Recognition
Modeling human activity from voxel person using fuzzy logic
IEEE Transactions on Fuzzy Systems
Registering aerial video images using the projective constraint
IEEE Transactions on Image Processing
Neighboring image patches embedding for background modeling
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Local histogram of figure/ground segmentations for dynamic background subtraction
EURASIP Journal on Advances in Signal Processing
Background subtraction for automated multisensor surveillance: a comprehensive review
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
International Journal of Computer Vision
Local spatial co-occurrence for background subtraction via adaptive binned kernel estimation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
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Background subtraction is a widely used paradigm to detect moving objects in video taken from a static camera and is used for various important applications such as video surveillance, human motion analysis, etc. Various statistical approaches have been proposed for modeling a given scene background. However, there is no theoretical framework for choosing which features to use to model different regions of the scene background. In this paper we introduce a novel framework for feature selection for background modeling and subtraction. A boosting algorithm, namely RealBoost, is used to choose the best combination of features at each pixel. Given the probability estimates from a pool of features calculated by Kernel Density Estimate (KDE) over a certain time period, the algorithm selects the most useful ones to discriminate foreground objects from the scene background. The results show that the proposed framework successfully selects appropriate features for different parts of the image.