By combining Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP) as the feature set, we pro- pose a novel human detection approach capable of handling partial occlusion. Two kinds of detectors, i.e., global de- tector for whole scanning windows and part detectors for local regions, are learned from the training data using lin- ear SVM. For each ambiguous scanning window, we con- struct an occlusion likelihood map by using the response of each block of the HOG feature to the global detector. The occlusion likelihood map is then segmented by Mean- shift approach. The segmented portion of the window with a majority of negative response is inferred as an occluded region. If partial occlusion is indicated with high likelihood in a certain scanning window, part detectors are applied on the unoccluded regions to achieve the fi nal classifi ca- tion on the current scanning window. With the help of the augmented HOG-LBP feature and the global-part o

An HOG-LBP Human Detector with Partial Occlusion Handling.pdf

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