Pedestrian Traffic Light Detection in Complex Scene Using Adaboost with Multi-layer Features
Abstract
In order to improve the accuracy of the pedestrian traffic light detection in complex scene, an
image detection method using AdaBoost with multi-layer features is proposed. In the proposed
method, the multi-layer features are adopted to characterize the pedestrian traffic lights, and the
AdaBoost algorithm is used to extract the discriminative multi-layer features automatically. The
multi-layer features consist of luminance and chrominance components, in which the luminancelayer
features are to grasp the shape information, and the chrominance-layer features are to
acquire color information. Based on the numerous features, hundreds of efficient weak classifiers
are selected by the AdaBoost algorithm to construct a strong classifier. With the strong classifier,
images are scanned in test procedure for detection of pedestrian traffic light. Testing results show
that the proposed multi-layer features in the CIELAB color space greatly improve the accuracy of
the pedestrian traffic light detection, and the proposed methods result in much better performance
than the state-of-the-art machine learning methods.
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