Implementation of real-time deep learning tracking algorithm based on NPU

The deep learning has developed rapidly in the field of detection, but limited by the training data and computing efficiency, the intelligent algorithms of deep learning are not widely used in the edge computing field based on embedded platform, especially in real-time tracking applications. Aiming at this phenomenon, in order to meet the needs of domestic and intelligent technology at the present stage, an improved twin network deep learning tracking algorithm was implemented. The fine-tuning network was added to the feature network to solve the problem that the network model could not be updated online and improve the accuracy of tracking. The center distance penalty term was added into IoUNet loss function to solve the problems of position jumping, existence of convergence blind area and the slow convergence when IoU was the same. The trained network was pruned through channels to reduce the size of network model and improved the loading and running speed of the model. Finally, the model was implemented in real-time on Huawei Atlas200NPU platform. The proposed algorithm accuracy is up to 0.90 （IoU>0.7）, and the frame rate reaches 66 Hz.