基于核密度估计的遮挡人体跟踪

充分利用空间、颜色、运动等信息对图像进行块建模、颜色建模和运动建模.通过混合高斯建模法,将运动人体的前景信息提取出来；基于Epanechnikov核密度梯度估计算法,对存储模型中的人体进行聚类,实现块建模；采用非参数的核密度估计算法和基于高斯分布的运动建模,分别获取颜色密度函数和运动密度函数,并利用颜色密度函数和运动密度函数对当前帧的前景区域进行后验概率估计,以获取后验概率图像,根据对该图像中遮挡人体进行分割以实现人体目标的跟踪.实验结果表明:基于核密度估计的遮挡人体目标跟踪算法有效地解决了遮挡人体目标跟踪问题.

Tracking human under occlusion based on kernel density estimation

The blob model, color model and motion model were built by fully using the space information, the motion information and the color information. The moving foreground targets based on mixture Gaussian model were detected. The people body in stored model was clustered to acquire the blob model based on Epanechnikov kernel density estimation algorithm. Color density function using non-parametric kernel density estimation algorithm and motion density function using Gauss distribution were obtained. The posterior probability images were attained by estimating the foreground of the current frame using the color density function and the motion density function. Then the occluded people in the posterior probability images were segmented. Finally, the results after segmenting occluded people were the final tracking targets. Experimental results show that the proposed algorithm efficiently solves the problem of tracking people under occlusion