摘要
提出一种基于广义霍夫变换的室外场景行人检测方法。首先从少量标注图片中随机地提取行人图像碎片构造碎片字典,然后使用图像碎片对每一幅训练图片计算特征向量。为了能够在静态图片中快速地检测行人,使用Gentleboost算法训练检测器,在每一次迭代时学习一个决策树桩弱分类器,该弱分类器可以从高维特征向量中选择一个当前区分度最好的碎片特征。在运行检测器时,所有的弱分类器在测试图片中对于行人的可能出现位置进行投票。最后,将各个弱分类器的投票结果进行叠加,并用设定的检测阈值剔除得分较低的检测结果后得到检测输出。在Label Me数据集上的实验表明,该方法可以快速地在静态图片中检测出行人,需要较少的训练数据且有效地解决了部分遮挡问题。
A novel outdoor scene human detection approach based on generalized Hough transform is proposed in this paper. Firstly, we randomly extract fragments of human instances from annotated training images and build a fragment dictionary. Then we utilize these extracted fragments to compute feature vectors for each training image. In order to rapidly detect human in a static image, Gentleboost algorithm is used to train detectors. In each round of boosting, a regression decision stump is learned as the weak classifier which can pick the most distinctive fragment features from the high dimensional fragment feature vector. When running the trained human detector, all the weak classifiers voted for the possible positions of human instances in a given test im- age. Finally, the output positions of all the weak classifiers are accumulated and some low score outputs are eliminated using a manually specified threshold to get the final detection outputs. Experiments on LabelMe datasets show that this approach can rap- idly detect human instances from static image using relative fewer training images and can effectively solve the partial occlusion problem.
出处
《计算机与现代化》
2015年第4期70-73,77,共5页
Computer and Modernization
关键词
行人检测
广义霍夫变换
碎片特征
室外场景
human detection
generalized Hough transform
fragment features
outdoor scene
