A real-time high-precision object tracking algorithm based on SiamFC for UAV images

Abstract

The object tracking algorithms based on deep learning represented by SiamFC have demonstrated promising tracking capabilities. However, convolution networks take up a lot of memory, and it is difficult to run in real-time tracking on UAV platforms. Targeting this issue, we propose an object tracking algorithm called SiamUAV based on the siamese network in this paper. Firstly, based on the backbone network of the SiamFC algorithm, depthwise separable convolution is adopted to improve the tracking speed. Secondly, a spatial and channel squeeze & excitation block is introduced as an attention mechanism so that the backbone network can dynamically adjust to improve the tracking performance. Lastly, the algorithm is deployed on the NVIDIA Jetson AGX Xavier embedded platform with acceleration by TensorRT. The algorithm achieves essentially the same accuracy as the SiamFC algorithm. The tracking speed is improved by more than 70%, reaching 59 FPS on the embedded platform. This provides an excellent tracking speed while ensuring tracking accuracy.