Real-time pedestrian detection on a xilinx zynq using the HOG algorithm
Jens Rettkowski, Andrew Boutros, Diana Göhringer · 2015
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 %.