Target tracking algorithm has been widely used in many fields. However, due to the problems of real-time and power consumption, it is difficult to deploy the algorithm based on deep learning model on mobile terminal devices. This work studies the deployment strategy of target tracking algorithm on mobile devices from the perspective of application deployment optimization combined with edge computing technology. A deployment strategy of target tracking application oriented to edge computing is proposed based on the analysis of device characteristics and edge cloud network architecture. The computing task of target tracking application is reasonably unloaded to edge cloud by task segmentation strategy and the computing results are analyzed and fused by the information fusion strategy. In addition, a motion detection scheme is proposed to further reduce the computing pressure and power consumption of terminal nodes The experimental results show that compared with local computing, the deployment strategy significantly reduces the response time of the task, and compared with completely uninstalling to the edge cloud, the deployment strategy reduces the processing time of the same computing task.