We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The detection pipeline is composed by the following state-of-the-art algorithms: features extracted from the input image are Histograms of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG); candidate generation using Pyramidal Sliding Window technique; and classification with Support Vector Machine (SVM). Experimental results show that the Tegra ARM platform is two times more energy efficient than a desktop GPU and at least 8 times faster than a desktop multicore CPU.
|Name||International Conference on Computational Science 2016, ICCS 2016|
|Publisher||Procedia Computer Science|
- Autonomous Driving
- pedestrian detection
- Computer Vision