GPU-based pedestrian detection for autonomous driving

V. Campmany, S. Silva, A. Espinosa, J. C. Moure, D. Vázquez, A. M. López

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35 Cites (Scopus)

Resum

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.

Idioma originalEnglish
Pàgines (de-a)2377-2381
Nombre de pàgines5
RevistaProcedia Computer Science
Volum80
DOIs
Estat de la publicacióPublicada - 2016

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