Advanced driver assistance systems (ADAS) are the key to enable autonomous cars in the near future. One important task for autonomous cars is to detect pedestrians reliably in real-time. The HOG algorithm is one of the best algorithms for this task; however it is very compute intensive. To fulfill the real-time requirements for high resolution images an efficient parallel implementation is necessary. This paper presents an efficient hardware implementation as well as a parallel software implementation of the HOG algorithm for pedestrian detection on a Xilinx Zynq SoC. The hardware implementation achieves a speedup of 2x compared to the parallel software implementation for high resolution images (1920 x 1080). Against state-of-the-art a speedup of 1.32x is achieved. The hardware implementation has a reliable detection rate of 90.2% using a classifier trained by an AdaBoost algorithm and a minor false positive rate of 4 %.
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机译:先进的驾驶员辅助系统(ADAS)是在不久的将来实现自动驾驶汽车的关键。自动驾驶汽车的一项重要任务是实时可靠地检测行人。 HOG算法是完成此任务的最佳算法之一。但是,这非常耗费计算资源。为了满足高分辨率图像的实时要求,有效的并行实现是必要的。本文介绍了用于Xilinx Zynq SoC上的行人检测的HOG算法的高效硬件实现以及并行软件实现。与高分辨率图像(1920 x 1080)的并行软件实现相比,硬件实现了2倍的加速。与最新技术相比,可实现1.32倍的加速。使用由AdaBoost算法训练的分类器,硬件实现的可靠检测率为90.2%,次要的误报率为4%。
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