J. Radmer and J. Krger (Germany)
Tracking, Machine Vision, Moving Object Detection, 2 1 2 D
For man-machine cooperation the detection of pose changes appearing as object motion is a crucial factor. Pre veous approaches were based on luminance and chromi nance data used for subsequent pose information extrac tion. In contrast we present an algorithm for the detection of moving objects for dynamic 2 1 2 D data providing direct spatial information based on the non-parametric model ap proach proposed by Elgammal [3]. The data is obtained by a range camera of static pose viewing the scene. Giv ing 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 ap proach utilizing the probability estimate.
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