General object detection method by on-board computer vision with Artificial Neural Networks

Jittima Varagul, Toshio Ito

研究成果: Article

抄録

The objective of this paper is to find object based solutions for a collision avoidance system. In this paper, the authors present an algorithm for obstacle detection, from the actual video images taken by an on-board camera. The proposed technique is based on Histograms of Oriented Gradient (HOG) to extract features of the objects and classify the obstacles by the Time Delay Neural Network (TDNN). The experimental results showed that it can detect general objects, and is not restricted to vehicles, objects or pedestrians. It has provided good results along with high accuracy and reliability.

元の言語English
ページ(範囲)149-156
ページ数8
ジャーナルInternational Journal of Automotive Engineering
8
発行部数4
DOI
出版物ステータスPublished - 2017 1 1

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Collision avoidance
neural network
Computer vision
Time delay
Cameras
Neural networks
pedestrian
video
Object detection

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Fluid Flow and Transfer Processes

これを引用

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abstract = "The objective of this paper is to find object based solutions for a collision avoidance system. In this paper, the authors present an algorithm for obstacle detection, from the actual video images taken by an on-board camera. The proposed technique is based on Histograms of Oriented Gradient (HOG) to extract features of the objects and classify the obstacles by the Time Delay Neural Network (TDNN). The experimental results showed that it can detect general objects, and is not restricted to vehicles, objects or pedestrians. It has provided good results along with high accuracy and reliability.",
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KW - Safety

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