To satisfy the need of video moving object locating in intelligent video surveillance scenes, video moving object locating technology based on nonparametric kernel density estimation is proposed. Nonparametric density estimation operation is firstly used on each foreground sample point that stands for video moving objects, and the sample point which has maximum density values is chosen as the first object center. And then other positions of video moving objects are gradually located by modifying the density value of sample point. This object locating method based on kernel density estimation is a further development of subtractive clustering object locating method. Using this method, kernel function could be flexibly chosen to estimate sample point’s density values according to different locating application scenes. Experiment results show that the proposed method has much more flexibility in sample point’s density estimation with satisfying locating results.