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
Robust Computer Vision through Kernel Density Estimation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Compressive Sensing for Background Subtraction
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Decoding by linear programming
IEEE Transactions on Information Theory
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
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Video sequences are viewed as a temporal collection of inverse problems. This parallel with the classical inverse problem of denoising brings us to investigate a sparse representation based approach for background subtraction. A global trained dictionary is obtained using a k-means classifier and using the matching pursuit method a set of coefficients is estimated. By linear combination of dictionary vectors (atoms) and the set of coefficients a background estimate is computed for each frame to obtain the foreground-background segmentation. The global dictionary and the coefficients are propagated and updated along the sequence. The approach yields surprisingly preliminary results, encouraging for further investigations on the possible extensions of the algorithm.