We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for candidate generation; and Support Vector Machine (SVM) for classification. Results show a 8x speedup in the target Tegra X1 platform and a better performance/watt ratio than desktop CUDA platforms in study.
@article{arxiv.1611.01642,
title = {GPU-based Pedestrian Detection for Autonomous Driving},
author = {Victor Campmany and Sergio Silva and Antonio Espinosa and Juan Carlos Moure and David Vázquez and Antonio M. López},
journal= {arXiv preprint arXiv:1611.01642},
year = {2016}
}