提出一种图像中人体快速自动检测方法.提取图像的多尺度-多形状方向梯度直方网(HOG)特征向量,用于描述人体的形状特征,结合Adaboost机器学习法训练级联型分类器,以加速人体的检测过程.相比较传统算法,该方法没有采用静态背景模型,也不是仅仅依赖于易受外部环境因索干扰的颜色信息,从而一定程度地适应了人体姿态变化,以及非结构化环境下常见的光照波动、背景杂乱等不良因素所带来的干扰.实验验证了该方法的准确性和较高的计算效率.%A fast and automatic people detection method is proposed. The multi-scale and multi-shape histogram of oriented gradient (HOG) features are extracted, which serve as a powerful description of human shapes;The extracted features are then fed into a cascade of classifiers trained by Adabcost algorithm to greatly accelerate the people detection scheme. The proposed method is independent from background models as well as color information in images, which is highly unreliable due to disturbance. This method is robust agains: human posture variances, lightening fluctuations as well as background cluttering. Experimental results validate the favorable performance of high accuracy and computational efficiency of the proposed method.
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