• KIPS Transactions on Software and Data Engineering
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• v.11 no.10
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• pp.409-418
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• 2022

In this paper, we propose SAAnnot-C3Pap, a semi-automatic annotation method for obtaining ground truth of a player's posture. In order to obtain ground truth about the two-dimensional joint position in the existing music domain, openpose, a two-dimensional posture estimation method, was used or manually labeled. However, automatic annotation methods such as the existing openpose have the disadvantages of showing inaccurate results even though they are fast. Therefore, this paper proposes SAAnnot-C3Pap, a semi-automated annotation method that is a compromise between the two. The proposed approach consists of three main steps: extracting postures using openpose, correcting the parts with errors among the extracted parts using supervisely, and then analyzing the results of openpose and supervisely. Perform the synchronization process. Through the proposed method, it was possible to correct the incorrect 2D joint position detection result that occurred in the openpose, solve the problem of detecting two or more people, and obtain the ground truth in the playing posture. In the experiment, we compare and analyze the results of the semi-automated annotation method openpose and the SAAnnot-C3Pap proposed in this paper. As a result of comparison, the proposed method showed improvement of posture information incorrectly collected through openpose.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.234-242
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• 2019

In this paper, we propose a deep neural network which extracts SIFT feature points by determining whether the center pixel of a cropped image is a SIFT feature point. The data set of this network consists of a DIV2K dataset cut into $33{\times}33$ size and uses RGB image unlike SIFT which uses black and white image. The ground truth consists of the RobHess SIFT features extracted by setting the octave (scale) to 0, the sigma to 1.6, and the intervals to 3. Based on the VGG-16, we construct an increasingly deep network of 13 to 23 and 33 convolution layers, and experiment with changing the method of increasing the image scale. The result of using the sigmoid function as the activation function of the output layer is compared with the result using the softmax function. Experimental results show that the proposed network not only has more than 99% extraction accuracy but also has high extraction repeatability for distorted images.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.3
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• pp.1-13
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• 2024

This paper proposes a cost-effective system design and user-friendly approach for the key technological elements necessary to configure an autonomous mobile robot. To implement a high-precision positioning system using an autonomous mobile robot, we established a Linux-based VRS (virtual reference station)-RTK (real-time kinematic) GNSS (global navigation satellite system) system with NTRIP (Network Transport of RTCM via Internet Protocol) client functionality. Notably, we reduced the construction cost of the GNSS positioning system by performing dynamic location analysis of the established system, without utilizing an RTK replay system. Dynamic location analysis involves sampling each point during the trajectory following of the autonomous mobile robot and comparing the location precision with ground-truth points. The proposed system ensures high positioning performance with fast sampling times and suggests a GPS waypoint system for user convenience. The centimeter-level precision GNSS information is provided at a 30Hz sampling rate, and the dead reckoning function ensures valid information even when passing through tall buildings and dense forests. The horizontal position error measured through the proposed system is 6.7cm, demonstrating a highly precise dynamic location measurement error within 10cm. The VRS network-RTK Linux system, which provides precise dynamic location information at a high sampling rate, supports a GPS waypoint planner function for user convenience, enabling easy destination setting based on GPS information.