关键词: PYNQ框架, 目标跟踪, 深度卷积特征, Zynq, 加速

Abstract:

In order to solve the problems of feature extraction in deep convolutional features visual tracking algorithm, such as large amount of computation, slow speed and difficulty in application on embedded platform, a visual tracking scheme based on PYNQ framework is proposed and deployed on Zynq heterogeneous platform. Firstly, a visual tracking algorithm based on deep convolutional features is designed. Then, according to the characteristics of the algorithm, the software and hardware are divided to complete the system-on-chip construction. Then, the calculation process of deep convolutional features extraction is optimized in parallel and derived as an accelerated IP core. Finally, via Jupyter Notebooks in the PYNQ framework, the accelerated IP core can be used as a hardware coprocessor to achieve data interaction from the bottom to the top. The experimental results show that the algorithm achieves good tracking accuracy on the benchmark OTB-2015 and UAV123. The tracking speed is up to 30 times higher than when the accelerated IP core is not integrated. Under the circumstance of considering the tracking robustness, the heterogeneous tracking system has high execution efficiency, good portability and engineering application value.

Key words: PYNQ framework, object tracking, deep convolutional features, Zynq, accelerate