W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Estimation of Range Flow on Deformable Shape From a Video Rate Range Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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For man-machine cooperation the detection of pose changes appearing as object motion is a crucial factor. Preveous approaches were based on luminance and chrominance data used for subsequent pose information extraction. In contrast we present an algorithm for the detection of moving objects for dynamic 2½D data providing direct spatial information based on the non-parametric model approach proposed by Elgammal [3]. The data is obtained by a range camera of static pose viewing the scene. Giving the coarse data captured by the camera the algorithm takes advantage of the dynamic and spatial characteristics of the data by evaluating temporal variation distribution. To counter long time changes, iterative updating of the used background model is performed by a selective updating approach utilizing the probability estimate.