Self-Driving vehicles are expected to thrive in the coming years. These vehicles are designed to analyze the environment around them in real-time to identify obstacles and hazards. One of the most important aspects of designing a self-driving vehicle is to preserve the safety of pedestrians. This requires accurate and rapid pedestrian detection, which is a key operation in various other applications including video surveillance and assisted living. The covariance descriptor is one of the most efficient descriptors used in detecting pedestrians. However, the descriptor is compute-intensive; rendering it less favorable for real-time applications. This paper proposes an accelerated and optimized implementation of the descriptor. Instead of mapping the entire descriptor to a hardware accelerator, we opt for a heterogeneous architecture. In particular, compute-intensive components of the descriptor are accelerated on hardware, while the other components are executed on an embedded processor. The proposed architecture combines both speed and flexibility while being watchful of precious hardware resources. This architecture was validated on a Zynq SoC platform, which hosts FPGA fabric along with an ARM processor. The results of executing the descriptor on the platforms show a performance gain of up to 13.52 × when compared to pure software implementation of the descriptor.

自动驾驶汽车有望在未来几年蓬勃发展。这些车辆旨在实时分析周围的环境，以识别障碍物和危险。设计自动驾驶汽车最重要的方面之一是保护行人的安全。这需要准确快速的行人检测，这是包括视频监控和辅助生活在内的各种其他应用的关键操作。协方差描述符是用于检测行人的最有效的描述符之一。但是，描述符是计算密集型的；使其不太适合实时应用程序。本文提出了描述符的加速和优化实现。我们选择异构架构，而不是将整个描述符映射到硬件加速器。尤其，描述符的计算密集型组件在硬件上加速，而其他组件在嵌入式处理器上执行。拟议的架构结合了速度和灵活性，同时注意宝贵的硬件资源。该架构在 Zynq SoC 平台上得到验证，该平台承载 FPGA 结构和 ARM 处理器。与描述符的纯软件实现相比，在平台上执行描述符的结果显示性能提升高达 13.52 倍。它承载 FPGA 结构和 ARM 处理器。与描述符的纯软件实现相比，在平台上执行描述符的结果显示性能提升高达 13.52 倍。它承载 FPGA 结构和 ARM 处理器。与描述符的纯软件实现相比，在平台上执行描述符的结果显示性能提升高达 13.52 倍。