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Moving target trajectory prediction is a typical multidisciplinary research issue involving intelligent science and technology and transportation engineering. Automatic tracking and shooting is a field with significant theoretical research and practical application values in the problem of moving target trajectory prediction. With the increasing demand for tracking shooting, the requirements for target tracking are also getting higher and higher, which has attracted widespread attention in recent years. In the last few years, automatic shooting has gained attention. It can automatically follow athletes and event training to provide more competition videos and also serve as a guiding tool for competition monitoring. Due to the complexity of the target trajectory, tracking shooting has gradually become one of the difficulties in the researches of the area. Most of the traditional methods of trajectory prediction use mathematical models to predict the states and behaviors of targets, however, the computational complexity of traditional methods is high and they show poor performance in real scenarios. The tracking shooting system proposed in this study converts the detected three-dimensional moving objects into two-dimensional images by joining the turntable, employing encoder-decoder model and using an end-to-end computing method based on long-term memory network, and makes full use of the position and action posture information of acquired moving objects, so that it can deeply mine the historical position information and behavioral habit semantics of moving targets and realize effective mapping with future trajectories, predict the position information at the future moment, and then control the turntable to realize the tracking and shooting of 3D moving targets.
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