FPGA Implementation of Real-Time Pedestrian Detection Using Normalization-Based Validation of Adaptive Features Clustering

研究成果: Article査読

3 被引用数 (Scopus)

抄録

Presently, many researchers and engineers have investigated autonomous driving, which has significantly influenced the revolution of artificial intelligence (AI). One of the critical challenges for autonomous driving is the inadequate precision of autonomous vehicles in detecting pedestrians, which is a major safety hazard to human beings. In this paper, a Field Programmable Gate Array (FPGA) demonstration system with a normalization-based validity index (NbVI) has been proposed for real-time pedestrian detection. The proposed algorithm can accurately detect pedestrians by calculating the Manhattan distance between the target histogram of oriented gradient (HOG) features and real-time pedestrian HOG features. In lieu of sophisticated circuit layout and substantial training burden with neuron computation circuit, the proposed detection system with adaptive features clustering is hardware-friendly and is capable of real-time pedestrian detection using fewer training images with high detection rate (up to 99.2\%). Moreover, the function execution time of pedestrian detection is shortened by 25\% using FPGA acceleration.

本文言語English
論文番号9018153
ページ(範囲)9330-9341
ページ数12
ジャーナルIEEE Transactions on Vehicular Technology
69
9
DOI
出版ステータスPublished - 2020 9

ASJC Scopus subject areas

  • 自動車工学
  • 航空宇宙工学
  • 電子工学および電気工学
  • 応用数学

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