针对传统人数统计方法因遮挡、光照变化导致准确率低的问题，提出一种适用于深度图的模拟降水分水岭算法（Depth map based Rainfalling Watershed Segmentation，D-RWS）。修复深度图并用混合高斯背景建模提取前景。利用D-RWS算法分割深度图中感兴趣的行人头部区域（Region Of Interest，ROI）。采用质心欧式距离最短法关联各帧中同一目标并跟踪计数。实验结果表明：提出的方法准确率能够达到98%以上，平均每帧处理时间为25 ms（40 f/s），准确率和实时性可满足实际应用的要求。%To solve problems such as occlusion and illumination changes that lead to low accuracy on conventional methods, a novel method D-RWS(Depth map based Rainfalling Watershed Segmentation)is proposed. Depth map is inpainted and foreground is extracted with the help of mixture of Gaussian background model. D-RWS algorithm is used to segment head area as Region Of Interest(ROI). People are tracked and counted by analyzing trajectories, which associated by minimal Euclidean distance between the centers. Experimental results show that proposed people counting system can, on average, count people with an accuracy of 98%and operate at approximately 25 milliseconds per frame(40 f/s). The accuracy and real-time performance fully meet the requirements of practical application.
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