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GPU-based pedestrian detection for autonomous driving
Victor Campmany, Sergio Silva, Antonio Espinosa, Juan Carlos Moure, David Vazquez and Antonio Lopez. 2016. GPU-based pedestrian detection for autonomous driving. 16th International Conference on Computational Science.2377–2381.
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 foreground segmentation; 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.
GPU-based pedestrian detection for autonomous drivingVictor CampmanySergio SilvaAntonio EspinosaJuan Carlos MoureDavid VazquezAntonio Lopezopenurl:?ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Frefbase.cvc.uab.es%2F&genre=proceeding&title=GPU-based%20pedestrian%20detection%20for%20autonomous%20driving&date=2016&volume=80&spage=2377&epage=2381&aulast=Victor%20Campmany&au=Sergio%20Silva&au=Antonio%20Espinosa&au=Juan%20Carlos%20Moure&au=David%20Vazquez&au=Antonio%20Lopez&id=https%3A%2F%2Fdoi.org%2F10.1016%2Fj.procs.2016.05.455&sid=refbase%3ACVCVictor Campmany, Sergio Silva, Antonio Espinosa, Juan Carlos Moure, David Vazquez and Antonio Lopez. 2016. GPU-based pedestrian detection for autonomous driving. 16th International Conference on Computational Science.2377-2381.2016ConferencePapertextPedestrian detectionAutonomous DrivingCUDAurl:https://doi.org/10.1016/j.procs.2016.05.455file:http://refbase.cvc.uab.es/files/CSE2016.pdf16th International Conference on Computational Science20168023772381