Recently, the safety in vehicle also has become a hot topic as self-driving car is developed. In passive safety systems such as airbags and seat belts, the system is being changed into an active system that actively grasps the status and behavior of the passengers including the driver to mitigate the risk. Furthermore, it is expected that it will be possible to provide customized services such as seat deformation, air conditioning operation and D.W.D (Distraction While Driving) warning suitable for the passenger by using occupant information. In this paper, we propose robust vehicle occupant detection algorithm based on RGB-Depth-Thermal camera for obtaining the passengers information. The RGB-Depth-Thermal camera sensor system was configured to be robust against various environment. Also, one of the deep learning algorithms, OpenPose, was used for occupant detection. This algorithm is advantageous not only for RGB image but also for thermal image even using existing learned model. The algorithm will be supplemented to acquire high level information such as passenger attitude detection and face recognition mentioned in the introduction and provide customized active convenience service.

Abstract
 1. 서 론
 2. 본 론
  2.1 RGB-Depth-Thermal 카메라 구성
  2.2 OpenPose를 이용한 좌석 점유 판단
 3. 실 험
  3.1 RGB-Thermal 카메라 캘리브레이션 실험 결과
  3.2 실험 데이터베이스 수집 및 환경 별 분류
  3.3 좌석 점유 유무 검출 성능 평가
 4. 결 론
 References

• 송창호(Intelligent Robotics Research Center, Korea Electronics Technology Institute(KETI)) | Changho Song
• 김승훈(Intelligent Robotics Research Center, Korea Electronics Technology Institute(KETI)) | Seung-Hun Kim Corresponding author