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DNN Based Multi-spectrum Pedestrian Detection Method Using Color and Thermal Image
DNN Based Multi-spectrum Pedestrian Detection Method Using Color and Thermal Image
Journal of Broadcast Engineering. 2018. May, 23(3): 361-368
  • Received : March 30, 2018
  • Published : May 30, 2018
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Lee, Yongwoo
Shin, Jitae

Abstract
As autonomous driving research is rapidly developing, pedestrian detection study is also successfully investigated. However, most of the study utilizes color image datasets and those are relatively easy to detect the pedestrian. In case of color images, the scene should be exposed by enough light in order to capture the pedestrian and it is not easy for the conventional methods to detect the pedestrian if it is the other case. Therefore, in this paper, we propose deep neural network (DNN)-based multi-spectrum pedestrian detection method using color and thermal images. Based on single-shot multibox detector (SSD), we propose fusion network structures which simultaneously employ color and thermal images. In the experiment, we used KAIST dataset. We showed that proposed SSD-H (SSD-Halfway fusion) technique shows 18.18% lower miss rate compared to the KAIST pedestrian detection baseline. In addition, the proposed method shows at least 2.1% lower miss rate compared to the conventional halfway fusion method.
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