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Multi-Source Templates Learning for Real-Time Aerial Tracking

Aerial tracking aims at tracking an arbitrary visual object in a video captured by Unmanned Aerial Vehicles (UAV). Due to the scarce computation resources, the deployment of high-consuming state-of-the-art trackers on UAV becomes impractical. On the other hand, lightweight trackers suffer from inferior performance caused by the low sampling frequency and resolution of UAV videos. In this paper, we propose a novel multi-source templates learning method to alleviate the paradox of efficiency and effectiveness for aerial tracking. Besides conventional static and dynamic templates, our work introduces an additional general-object template to learn common feature properties of a general object during training time. To exploit all templates information, a multi-source templates fusion scheme is proposed to capture characteristics of object in low quality UAV video streams. Furthermore, a joint optimization process is employed to enforce the lightness of model while achieving comparable tracking performance. Our experimental results demonstrate an appealing performance trade-off between accuracy and speed. The proposed tracker achieves 200 FPS on GPU, 100 FPS on CPU, and 12 FPS on Nvidia Jetson Xavier NX, respectively. Our code will be released at https://github.com/vpx-ecnu/MSTL.

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