TY - GEN
T1 - Identification of deer in thermal images to avoid deer-vehicle crashes
AU - Zhou, Debao
AU - Wang, Jingzhou
PY - 2011/10/3
Y1 - 2011/10/3
N2 - Car accidents due to deer vehicle-crashes (DVCs) are constantly a major safety issue for the driving on rural roads in Europe and North America. Many attempts have been made to avoid these accidents, but few have been succeeded. One of the options is to use thermal images to identify the presence of deer, then to warn the drivers to slow down. In this research, we proposed using thermal images to determine whether deer are present or not. The histogram of orientated gradient (HOG) method and the support vector machine method (SVM) have been used to identify deer. In this algorithm, based on the calculation of the HOG on the thermal images of thousands of samples (deer thermal images), SVM method is firstly used to get the pattern of deer to generate deer description, called descriptor. Then the descriptor is used to compare with the HOG of the current image. The comparing result will tell the existence of deer in the current image. In order to improve accuracy, the second training, where the false positive images are used as training samples, has been performed. The lab and field test results have shown up to 85% accuracy. By achieving this goal, deer can be identified and warning signal to drivers can be issued. Thus the possibility of deer - vehicle crashes can be reduced.
AB - Car accidents due to deer vehicle-crashes (DVCs) are constantly a major safety issue for the driving on rural roads in Europe and North America. Many attempts have been made to avoid these accidents, but few have been succeeded. One of the options is to use thermal images to identify the presence of deer, then to warn the drivers to slow down. In this research, we proposed using thermal images to determine whether deer are present or not. The histogram of orientated gradient (HOG) method and the support vector machine method (SVM) have been used to identify deer. In this algorithm, based on the calculation of the HOG on the thermal images of thousands of samples (deer thermal images), SVM method is firstly used to get the pattern of deer to generate deer description, called descriptor. Then the descriptor is used to compare with the HOG of the current image. The comparing result will tell the existence of deer in the current image. In order to improve accuracy, the second training, where the false positive images are used as training samples, has been performed. The lab and field test results have shown up to 85% accuracy. By achieving this goal, deer can be identified and warning signal to drivers can be issued. Thus the possibility of deer - vehicle crashes can be reduced.
KW - Deer-Vehicle Crashes
KW - HOG and SVM
KW - Pattern Recognition
KW - Thermal imaging
UR - http://www.scopus.com/inward/record.url?scp=80053286882&partnerID=8YFLogxK
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U2 - 10.1109/ICEOE.2011.6013376
DO - 10.1109/ICEOE.2011.6013376
M3 - Conference contribution
AN - SCOPUS:80053286882
SN - 9781612842738
T3 - ICEOE 2011 - 2011 International Conference on Electronics and Optoelectronics, Proceedings
BT - ICEOE 2011 - 2011 International Conference on Electronics and Optoelectronics, Proceedings
T2 - 2011 International Conference on Electronics and Optoelectronics, ICEOE 2011
Y2 - 29 July 2011 through 31 July 2011
ER -