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