  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.
