TY - JOUR
T1 - General object detection method by on-board computer vision with Artificial Neural Networks
AU - Varagul, Jittima
AU - Ito, Toshio
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Automatic collision notification
KW - Image processing / neural networks
KW - Object detection [C1]
KW - Safety
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U2 - 10.20485/jsaeijae.8.4_149
DO - 10.20485/jsaeijae.8.4_149
M3 - Article
AN - SCOPUS:85039151067
SN - 2185-0984
VL - 8
SP - 149
EP - 156
JO - International Journal of Automotive Engineering
JF - International Journal of Automotive Engineering
IS - 4
ER -