GPU-based Pedestrian Detection for Autonomous Driving
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Nov 2016.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2.
4 papers.
- 2022Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyondcited by 8
- 2023Rethinking Cross-Domain Pedestrian Detection: A Background-Focused Distribution Alignment Framework for Instance-Free One-Stage Detectorscited by 5
- 2021C^3Net: End-to-End deep learning for efficient real-time visual active camera controlcited by 0
- 2023Continual Learning for Out-of-Distribution Pedestrian Detectioncited by 0
- 2023Rethinking Cross-Domain Pedestrian Detection: A Background-Focused Distribution Alignment Framework for Instance-Free One-Stage Detectors15 Sep 2023
- 2023Continual Learning for Out-of-Distribution Pedestrian Detection26 Jun 2023
- 2022Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond10 Jan 2022
- 2021C^3Net: End-to-End deep learning for efficient real-time visual active camera control28 Jul 2021