TY - GEN
T1 - Vehicle detection from onboard camera using patch decided vanishing point
AU - Wang, Zihao
AU - Qu, Weidong
AU - Kamata, Sei Ichiro
N1 - Funding Information:
This paper is supported by the Shaanxi Natural Science Foundation Project (2017JM6101), Fundamental Research Funds for the Central Universities (GK201703060)
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - In this paper, a vision-based vehicle detection using vanishing point is proposed, which could detect vehicles with different orientation using different models, and increases the accuracy. With the input image from the on-board camera, the method first divides it into small square patches. Then two approaches are used for grey image patches and color image patches which help to find out useful patches for vanishing point vote process. After that, the method uses selective search to generate candidates that might be vehicles. And with the vanishing point, orientation and scale of candidates are obtained. According to these, they are put into corresponding models which are trained offline. A new data set included orientation is also created for vehicles and non-vehicles which are used to do the experiment. The result shows that the method works and improves the accuracy.
AB - In this paper, a vision-based vehicle detection using vanishing point is proposed, which could detect vehicles with different orientation using different models, and increases the accuracy. With the input image from the on-board camera, the method first divides it into small square patches. Then two approaches are used for grey image patches and color image patches which help to find out useful patches for vanishing point vote process. After that, the method uses selective search to generate candidates that might be vehicles. And with the vanishing point, orientation and scale of candidates are obtained. According to these, they are put into corresponding models which are trained offline. A new data set included orientation is also created for vehicles and non-vehicles which are used to do the experiment. The result shows that the method works and improves the accuracy.
KW - Histogram algorithm
KW - Orientation based Vehicle detection
KW - Patch based vanishing point
KW - Self-made data set
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U2 - 10.1109/CISP-BMEI.2017.8302022
DO - 10.1109/CISP-BMEI.2017.8302022
M3 - Conference contribution
AN - SCOPUS:85047468633
T3 - Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
SP - 1
EP - 7
BT - Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
A2 - Li, Qingli
A2 - Wang, Lipo
A2 - Zhou, Mei
A2 - Sun, Li
A2 - Qiu, Song
A2 - Liu, Hongying
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
Y2 - 14 October 2017 through 16 October 2017
ER -