Spinning targets are usually observed in videos. The targets may sometimes appear as mobile targets at the same time. The targets become mobile spinning targets. Tracking a single point on a target is easier than tracking the whole target. We use a characteristic point on the target to estimate the interested parameters, such as angular velocity, virtual rotation center and moving velocity. Among these parameters, virtual rotation center does not spin, therefore it can be used to represent the position of the target. Traditionally, extended Kalman filter (EKF), unscented Kalman filter (UKF) and particle filter (PF) are choices for solving the nonlinear problems, but some problems exist. Linearization errors cause that EKF cannot accurately estimate the angular velocity. UKF and PF have high computational complexity. In the thesis, we give angular velocity an initial value. So we can establish a linear dynamic system model to displace the nonlinear model. Then, a new structure is proposed to avoid errors caused by initial value of angular velocity. In the structure, angular velocity is estimated individually and used to correct the initial value by feedback. We try to use fast Fourier transform to estimate angular velocity. But the convergence time of this method is affected by the value of angular velocity, and the direction of angular velocity can not be estimated directly. Therefore, Kalman filter (KF) with pseudo measurement is proposed to estimate the value of angular velocity. The estimator is accurate and has low computational complexity. Once angular velocity is estimated, we can easily predict the virtual rotation center from geometric relationship. In video system, measurements may be quantized and targets may sometimes be obstacled. We fix the measurement equation and use KF to mitigate quantization error. When measurements for the target is missing, the previous state is used to predict the current state. Finally, computer simulations are conducted to verify the effectiveless of the proposed method. The method can work in environments where measurement noise or quantization error exists. The methods can also be applied to different kinds of mobile spinning targets.