Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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A New Object Region Detection and Classification Method using Multiple Sensors on the Driving Environment

다중 센서를 사용한 주행 환경에서의 객체 검출 및 분류 방법

  • Kim, Jung-Un (Dept. of Media Eng., The Catholic University of Korea) ;
  • Kang, Hang-Bong (Dept. of Media Eng., The Catholic University of Korea)
  • Received : 2017.07.17
  • Accepted : 2017.07.31
  • Published : 2017.08.31
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Abstract

It is essential to collect and analyze target information around the vehicle for autonomous driving of the vehicle. Based on the analysis, environmental information such as location and direction should be analyzed in real time to control the vehicle. In particular, obstruction or cutting of objects in the image must be handled to provide accurate information about the vehicle environment and to facilitate safe operation. In this paper, we propose a method to simultaneously generate 2D and 3D bounding box proposals using LiDAR Edge generated by filtering LiDAR sensor information. We classify the classes of each proposal by connecting them with Region-based Fully-Covolutional Networks (R-FCN), which is an object classifier based on Deep Learning, which uses two-dimensional images as inputs. Each 3D box is rearranged by using the class label and the subcategory information of each class to finally complete the 3D bounding box corresponding to the object. Because 3D bounding boxes are created in 3D space, object information such as space coordinates and object size can be obtained at once, and 2D bounding boxes associated with 3D boxes do not have problems such as occlusion.

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