Online cascaded boosting with histogram of orient gradient features for car detection from unmanned aerial vehicle images

Ang Su; Xiaoliang Sun; Haibo Liu; Xiaohu Zhang; Qifeng Yu

doi:10.1117/1.JRS.9.096063

17 April 2015 Online cascaded boosting with histogram of orient gradient features for car detection from unmanned aerial vehicle images

Ang Su, Xiaoliang Sun, Haibo Liu, Xiaohu Zhang, Qifeng Yu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 096063 (April 2015). https://doi.org/10.1117/1.JRS.9.096063

Abstract

Car detection from unmanned aerial vehicle (UAV) images has become an important research field. However, robust and efficient car detection is still a challenging problem because of the cars’ appearance variations and complicated background. We present an online cascaded boosting framework with histogram of orient gradient (HOG) features for car detection from UAV images. First, the HOG of the whole sliding window is computed to find the primary gradient direction that is used to estimate the car’s orientation. The sliding window is then rotated according to the estimated car’s orientation, and the HOG features in the rotated window are efficiently computed using the proposed four kinds of integral histograms. Second, to improve the performance of the weak classifiers, a new distance metric is employed instead of the Euclidean distance. Third, we propose an efficient online cascaded boosting for car detection by combining online boosting with soft cascade. Additionally, for the problem of imbalanced training samples, more positive samples are extracted in the rotated images, and for postprocessing, a confidence map is obtained to combine multiple detections and eliminate isolated false negatives. A set of experiments on real images shows the applicability and high efficiency of the proposed car detection method.

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Ang Su, Xiaoliang Sun, Haibo Liu, Xiaohu Zhang, and Qifeng Yu "Online cascaded boosting with histogram of orient gradient features for car detection from unmanned aerial vehicle images," Journal of Applied Remote Sensing 9(1), 096063 (17 April 2015). https://doi.org/10.1117/1.JRS.9.096063

Published: 17 April 2015

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