Aiming at the problem that it is difficult to locate the camera automatically in an accurate and appropriate position for human motion capture in the existing technology

a method of uncertainty estimation based on unmanned aerial vehicle（UAV）control and optimal viewpoint selection is proposed.Firstly

the existing neural network is used to derive 2 D and 3 D human pose from the current frame of UAV video.Then

the 2 D pose and related 3 D pose of the past k frames are used to optimize the global 3 D human motion.Finally

the next optimal viewpoint is selected to minimize the uncertainty of human pose estimation from this viewpoint.Experimental results on synthetic and public datasets show that the proposed method can improve the 3 D human motion estimation results.The performance of the proposed method is better than that of the baseline method when the next viewpoint is selected without physical constraints.For the simulation of UAV flight mode

the performance of the proposed method is similar to that of the non-stop orbit method.