Research on Pedestrian's Detection Based on the Integration of AdaBoost Algorithm and Shape Features

In this paper, propose a pedestrian detection method that based on AdaBoost algorithm and pedestrian shape features integration. First according to the collected pedestrian true, false sample, selected the characteristics of the extended class Haar, adopt AdaBoost algorithm training get pedestrian classifier to split the initial candidate region of all pedestrians in the image. In this paper, propose an adaptive threshold weight update method, significantly reduced the number of the characteristics of strong classifier, optimize the classifier structure, reduce the complexity of the algorithm; meanwhile, the online update detector, improving the reliability of the detector. Pedestrian leg have strong vertical edge symmetry characteristic so that extracted the vertical edge detection in the initial candidate region, According to the symmetry determine the vertical axis of symmetry, combined with the morphological characteristics of pedestrians to determine the width and height characteristics of the pedestrian, to determine the pedestrian candidate region, Finally, put a further validation to the pedestrian candidate region.