Aiming at the problem that existing motion control strategies cannot guarantee high-precision control for independently driven caster-type omnidirectional mobile platform， a double closed-loop trajectory tracking control strategy was proposed by combining Model Predictive Control （MPC） and Proportion Integral Differential （PID） control. Firstly， kinematic geometric relationship was used to establish three-degree-of-freedom kinematic model of independently driven caster-type omnidirectional mobile platform in world coordinate system， and based on orthogonal decomposition method， inverse kinematic model of platform in robot coordinate system was established to reflect the relationship between center point speed of platform and rotation speed of each caster. Secondly， MPC was used to design position controller based on three-degree-of-freedom kinematic model， so that platform could track positions of desired trajectory， and the optimal control quantity was solved through the position controller while taking multi-objective constraints into account. Finally， PID was used to design speed controller to track desired speed output by position controller. Desired caster speed was calculated through the inverse kinematic model of platform， thereby driving platform to achieve omnidirectional motion. The effectiveness of the proposed control strategy was verified through simulation， and the platform could effectively track linear trajectories and circular trajectories. Simulation results show that compared with position single-loop trajectory tracking control strategy that decouples drive caster speed through angle inverse kinematic model of platform， the system overshoot is reduced by 97.23% and the response time is shortened by 36.84% after adding speed inner loop.

video-based human action recognition has received much attention recently in computer vision while it is a very challenging research with many applications such as automatic behavior analysis, visual surveillance and human-computer interaction. A human action recognition method based on cascaded structure is proposed in this paper. Firstly, a trajectory-based method is proposed to select the interest points detected by the Dollar detector, which is sensitive to image noise, camera movement and zooming. Therefore, the pseudo interest points in the background can be effectively excluded and the extracted features will be more relevant to action recognition. Secondly, an automatic feature selection method based on the combination of normalized cuts and mRMR criteria is used to determine a subset of the words generated by the Bag-of-Words model and construct a cascaded structure for action recognition. The purpose is to make the feature used by the cascaded structure more distinctive. Lastly, the promising experimental results validate our contribution to the improvement of accuracy in human action recognition.