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
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Modeling of Colour Data
International Journal of Computer Vision
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Modeling Scenes with Local Descriptors and Latent Aspects
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
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In this paper, foreground detection is performed by scene interpretation. A natural scene in different illumination conditions is characterized by scene context which contains spatial and appearance representations. The spatial representation is obtained in two steps. First, the large homogenous regions in each sample image are extracted using local and global dominant color histograms (DCH). Then, the latent semantic regions of the scene are generated by combining the coincident regions in the segmented images. The appearance representation is learned by the probabilistic latent semantic analysis (PLSA) model with local DCH visual words. The scene context is then applied to interpret incoming images from the scene. For a new image, its global appearance is first recognized and then the pixels are labelled under the constraint of the scene appearance. The proposed method has been tested on various scenes under different weather conditions and very promising results have been obtained.