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Even with the advent of more sophisticated, data-hungry methods, boosted decision trees remain extraordinarily successful for fast rigid object detection, achieving top accuracy on numerous datasets. While effective, most boosted detectors use decision trees with orthogonal (single feature) splits, and the topology of the resulting decision boundary may not(More)
Even with the advent of more sophisticated, data-hungry methods, boosted decision trees remain extraordinarily successful for fast rigid object detection, achieving top accuracy on numerous datasets. While effective, most boosted detectors use decision trees with orthogonal (single feature) splits, and the topology of the resulting decision boundary may not(More)
We propose a novel background subtraction algorithm for the videos captured by a moving camera. In our technique , foreground and background appearance models in each frame are constructed and propagated sequentially by Bayesian filtering. We estimate the posterior of appearance , which is computed by the product of the image likelihood in the current frame(More)
A macrofeature layout selection is proposed for object detection. Macrofeatures [2] are mid-level features that jointly encode a set of low-level features in a neighborhood. Our method employs line, triangle, and pyramid layouts, which are composed of several local blocks in a multi-scale feature pyramid. The method is integrated into boosting for(More)
We propose a novel algorithm to detect occlusion for visual tracking through learning with observation likelihoods. In our technique, target is divided into regular grid cells and the state of occlusion is determined for each cell using a classifier. Each cell in the target is associated with many small patches, and the patch likelihoods observed during(More)
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