Recently, smart factories have attracted much attention as a result of the 4th Industrial Revolution. Existing factory automation technologies are generally designed for simple repetition without using vision sensors. Even small object assemblies are still dependent on manual work. To satisfy the needs for replacing the existing system with new technology such as bin picking and visual servoing, precision and real-time application should be core. Therefore in our work we focused on the core elements by using deep learning algorithm to detect and classify the target object for real-time and analyzing the object features. We chose YOLO CNN which is capable of real-time working and combining the two tasks as mentioned above though there are lots of good deep learning algorithms such as Mask R-CNN and Fast R-CNN. Then through the line and inside features extracted from target object, we can obtain final outline and estimate object posture.

최근 스크린 클라이밍용 콘텐츠로 클라이밍 학습 프로그램과 스크린 클라이밍 게임이 등장하였으 며, 특히 스크린 클라이밍 게임에 대한 연구가 활발히 진행되고 있다. 본 논문에서는 스크린 클라이 밍 콘텐츠 구현의 핵심 기술인 자세 인식 성능의 개선을 위하여 등반자의 신체영역을 기반으로 하 는 스켈레톤 보정 방법을 제안한다. 스켈레톤 보정 과정은 비정상적인 스켈레톤 정보를 걸러내는 스켈레톤 프레임 안정화와 신체 영역을 관절부위별로 나누어 각 관절부위의 중점을 보정위치로 하 는 신체영역 기반 스켈레톤 수정 과정으로 이루어진다. 이렇게 보정한 스켈레톤 정보는 클라이밍 콘텐츠에서 등반자의 자세가 이상적인 자세와 얼마나 유사한지 판단하는 데 사용될 수 있다.

This paper presents a method of improving the pose recognition accuracy of objects by using Kinect sensor. First, by using the SURF algorithm, which is one of the most widely used local features point algorithms, we modify inner parameters of the algorithm for efficient object recognition. The proposed method is adjusting the distance between the box filter, modifying Hessian matrix, and eliminating improper key points. In the second, the object orientation is estimated based on the homography. Finally the novel approach of Auto-scaling method is proposed to improve accuracy of object pose estimation. The proposed algorithm is experimentally tested with objects in the plane and its effectiveness is validated.