Monocular Multi-Human Detection Using Augmented Histograms of Oriented Gradients
Date Issued
2007
Date
2007
Author(s)
Chuang, Cheng-Hsiung
Abstract
In this thesis we introduce an Augmented Histograms of Oriented Gradients (AHOG) feature for human detection from a non-static camera. This research tries to increase the discriminating power of original Histograms of Oriented Gradients (HOG) feature by adding human shape properties, such as contour distances, symmetry, gradient density, and shape approximation. The relations among AHOG features are characterized by the contour distances to the centroid of human. By observing on the biological structure of a human shape, we impose the symmetry property on every HOG feature and compute the similarity between feature itself and its symmetric pair so as to weigh HOG features. After that, the capability of describing human features is greatly improved when being compared with that of traditional one, especially when the moving humans are under consideration. Besides, we also augment the gradient density into AHOG to mitigate the influences caused by repetitive backgrounds. Moreover, we reject the false detections via an elliptical verifier learned when one tries to approximate a human shape. In the experiments, our proposed human detection method demonstrates highly reliable accuracy and provides the comparable performance to the state-of-the-art human detector on different databases.
Subjects
human detection
histograms of oriented gradients
AdaBoost
Type
thesis
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