基于深度信息的人体检测窗口快速提取方法

Studies of Fast Extraction Algorithm for People Detection Window Based on Depth Information

为改善图像尺度空间搜索方法在人体检测窗口提取中的检测窗口数量多、检测消耗时间长的问题,在深入分析Kinect深度数据特点的基础上,给出了一种基于深度信息快速提取人体检测窗口的方法.该方法通过计算待检测深度图像中深度值出现频次的极大值,根据出现频次极大值对应深度值区域的几何中心确定检测窗口候选位置的中心,并进一步基于深度值和人体高度间的关系,确定检测窗口的尺寸,从而实现快速提取一系列人体检测窗口.最后,从检测窗口数量、响应时间和准确度等3个方面对该提取方法进行了评估,结果表明:该方法的总体效果良好,缩短了检测时间,并且准确率较高,达到了提高人体检测效率的目的.

To improve the generalization performance of detection window extraction on people detection,a fast extraction algorithm based on depth information was proposed. The geometric centers of detection windows were obtained by calculating extreme values of frequency of depth data. And the sizes of detection windows were obtained based on the relationship between depth and height in depth image.Finally,a series of people detection windows were extracted fast. And the algorithm was evaluated from the number of detection windows,response time and the accuracy. Experimental results show the feasibility and efficiency of this method.