A real-time siamese tracker deployed on UAVs

Visual object tracking is an essential enabler for the automation of UAVs. Recently, Siamese network based trackers have achieved excellent performance on offline benchmarks. The Siamese network based trackers usually use classic deep and wide networks, such as AlexNet, VggNet, and ResNet, to extract the features of template frame and detection frame. However, due to the poor computing power of embedded devices, these models without modification are too heavy on calculation to be deployed on UAVs. In this paper, we propose a guideline to design a slim backbone: the dimension of output should be smaller than that of the input for every layer. Directed by the guideline, we reduce the computational requirements of AlexNet by 59.4%, while the tracker maintains a comparable accuracy. In addition, we adopt an anchor-free network as the tracking head, which requires less calculation than that of anchor-based method. Based on such approaches, our tracker achieves an AUC of 60.9% on UAV123 data set and reaches 30 frames per second on NVIDIA Jetson TX2, which, therefore, can be embedded in UAVs. To the best of our knowledge, it is the first real-time Siamese tracker deployed on the embedded system of UAVs. The code is available at GitHub.

Automated summary

This paper proposes a guideline for designing a lightweight backbone to tackle the poor computing power issue on embedded devices. The guideline reduces the computational requirements while maintaining accuracy. By adopting an anchor-free network, the tracker achieves good performance on UAV datasets and can be embedded in UAVs.