旋转角可变的人体检测算法

Human detection algorithm with variable rotation angle

常用的人体检测算法多应用于无旋转角的情况,而在旋转角可变的情况下检测性能有限,为此提出了一种适用于有旋转角的人体检测算法。首先,通过径向梯度转换(RGT)获得具有旋转不变性的梯度;其次,使用类似于梯度方向直方图(HOG)特征中相互重叠块的组合方式,获取多个带有旋转角信息的特征描述子,按旋转角大小将它们一维线性连接成具有旋转不变性的特征描述子组;最后,利用基于支持向量机(SVM)的二级级联分类器实现了带旋转角的人体检测。基于INRIA行人数据库的144个不同旋转角的人体测试集检测率都不低于86%,144个不同旋转角的非人体样本误检率均低于10%。实验证明了该算法可用于在任意旋转角图像上进行人体检测。

Prevalent human detection methods are usually applied in cases without rotation angle, and their detection rates are poor when rotation angle varies. In order to solve the issue, an algorithm which could identify human with variable rotation angle was proposed. Firstly, Radial Gradient Transform （RGT） method was adopted to obtain the rotation-invariance gradient. Then, adopting the method similar to the way that blocks were overlapped in the Histogram of Oriented Gradient （HOG） feature, a plurality of descriptors with rotation angle information were obtained and connected linearly into a descriptor group with rotation invariance feature, according to the descriptors＇ rotation angle. Finally, the human detection algorithm was conducted with the support of a two-level cascaded classifier based on Support Vector Machine （ SVM）. The recognition rate of the proposed algorithm achieves more than 86% for a human test set with 144 different rotation angles based on the INRIA pedestrian database. In the meantime, the false detection rate is less than 10% for a non-human test set with 144 different rotation angles. The experiments indicate that the proposed algorithm can be used for human detection in an image with arbitrary rotation angle.