TY  - JOUR
T1  - A Pedestrian Detection Method using Deep Neural Network
AU  - Song, Su Ho 
AU  - Hyeon, Hun Beom 
AU  - Lee, Hyun 
JO  - Journal of KIISE, JOK
PY  - 2017
DA  - 2017/1/14
DO  - 
KW  - faster R-CNN
KW  - pedestrian detection
KW  - deep learning
AB  - Pedestrian detection, an important component of autonomous driving and driving assistant system, has been extensively studied for many years. In particular, image based pedestrian detection methods such as Hierarchical classifier or HOG and, deep models such as ConvNet are well studied. The evaluation score has increased by the various methods. However, pedestrian detection requires high sensitivity to errors, since small error can lead to life or death problems. Consequently, further reduction in pedestrian detection error rate of autonomous systems is required. We proposed a new method to detect pedestrians and reduce the error rate by using the Faster R-CNN with new developed pedestrian training data sets. Finally, we compared the proposed method with the previous models, in order to show the improvement of our method.