• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2021.06a
• /
• pp.228-231
• /
• 2021

인물의 행동 및 이동을 인식하는 것은 다양한 분야에서 활용될 수 있다. 사람의 행동을 파악하여 니즈를 예상하고 맞춤형 콘텐츠를 제공하거나 행동을 예측하여 범죄나 폭력을 예방하는 등 여러 방면으로 활용 가능하다. 그러나 이동과 현재 위치 정보만으로 인물의 행동을 예측하기에는 한계가 있다. 본 논문에서는 실시간으로 사람의 이동과 행동을 인식하기 위해 Kinect v2가 제공하는 관절 정보와 YOLOv3를 이용하여 실시간으로 사람의 행동을 인식하는 시스템을 제작하였다.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.3
• /
• pp.45-52
• /
• 2022

A tangible sports game is an exercise game that uses sensors or cameras to track the user's body movements and to feel a sense of reality. Recently, VR indoor sports room systems installed to utilize tangible sports game for physical activity in schools. However, these systems primarily use screen-touch user interaction. In this research, we developed a V-Catch Vision system that uses AI image recognition technology to enable tracking of user movements in three-dimensional space rather than two-dimensional wall touch interaction. We also conducted a usability evaluation experiment to investigate the exercise effects of this system. We tried to evaluate quantitative exercise effects by measuring blood oxygen saturation level, the real-time ECG heart rate variability, and user body movement and angle change of Kinect skeleton. The experiment result showed that there was a statistically significant increase in heart rate and an increase in the amount of body movement when using the V-Catch Vision system. In the subjective evaluation, most subjects found the exercise using this system fun and satisfactory.

• Journal of Korea Multimedia Society
• /
• v.19 no.12
• /
• pp.1927-1935
• /
• 2016

This paper propose a framework for human body parts in RGB-D image. We conduct tasks of obtaining person area, finding candidate areas and local detection in order to detect hand, foot and head which have features of long accumulative geodesic distance. A person area is obtained with background subtraction and noise removal by using depth image which is robust to illumination change. Finding candidate areas performs construction of graph model which allows us to measure accumulative geodesic distance for the candidates. Instead of raw depth map, our approach constructs graph model with segmented regions by quadtree structure to improve searching time for the candidates. Local detection uses HOG based SVM for each parts, and head is detected for the first time. To minimize false detections for hand and foot parts, the candidates are classified with upper or lower body using the head position and properties of geodesic distance. Then, detect hand and foot with the local detectors. We evaluate our algorithm with datasets collected Kinect v2 sensor, and our approach shows good performance for head, hand and foot detection.

• The Journal of Korea Robotics Society
• /
• v.13 no.4
• /
• pp.230-236
• /
• 2018

3D depth perception has played an important role in robotics, and many sensory methods have also proposed for it. As a photodetector for 3D sensing, single photon avalanche diode (SPAD) is suggested due to sensitivity and accuracy. We have researched for applying a SPAD chip in our fusion system of time-of-fight (ToF) sensor and stereo camera. Our goal is to upsample of SPAD resolution using RGB stereo camera. Currently, we have 64 x 32 resolution SPAD ToF Sensor, even though there are higher resolution depth sensors such as Kinect V2 and Cube-Eye. This may be a weak point of our system, however we exploit this gap using a transition of idea. A convolution neural network (CNN) is designed to upsample our low resolution depth map using the data of the higher resolution depth as label data. Then, the upsampled depth data using CNN and stereo camera depth data are fused using semi-global matching (SGM) algorithm. We proposed simplified fusion method created for the embedded system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.394-395
• /
• 2022

Exergames are playing an important role in healthcare/rehabilitation. It has also been used to improve motivation among patients with reduced cognition. The purpose of this pilot study was to evaluate the feasibility of using augmented reality (AR) with game-based cognitive-motor training programs for executive function, restricted and repetitive behaviors (RRBs) in children with autism spectrum disorder. Sixteen children aged 6 -16 years were randomly allocated to the experimental group and control group. Outcome measures were performed before and after the intervention and included executive function, restricted and repetitive behavior. A satisfactory survey was conducted post-intervention. A statistically significant improvement was observed in working memory and cognitive flexibility in the experimental group (P<0.05). However, despite no statistical improvements in cognitive inhibition and four subscales of RRBs, promising changes were observed in all the subscales of the executive function and the behavioral outcomes. Parents appreciated the program and children enjoyed the interaction with the AR game-based training. The findings of this preliminary feasibility study showed that AR using Kinect v2 motion with a cognitive-motor game content can be used for children with autism. However, there is a need for conducting a large-scale study to evaluate his effectiveness on executive function and restricted and repetitive behaviors.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.27 no.2
• /
• pp.126-131
• /
• 2017

In this paper, we propose a method to extract meaningful motion among various kinds of hand gestures on giving commands to robots using hand gestures. On giving a command to the robot, the hand gestures of people can be divided into a preparation one, a main one, and a finishing one. The main motion is a meaningful one for transmitting a command to the robot in this process, and the other operation is a meaningless auxiliary operation to do the main motion. Therefore, it is necessary to extract only the main motion from the continuous hand gestures. In addition, people can move their hands unconsciously. These actions must also be judged by the robot with meaningless ones. In this study, we extract human skeleton data from a depth image obtained by using a Kinect v2 sensor and extract location data of hands data from them. By using the Kalman filter, we track the location of the hand and distinguish whether hand motion is meaningful or meaningless to recognize the hand gesture by using the hidden markov model.

• Journal of Broadcast Engineering
• /
• v.20 no.3
• /
• pp.414-420
• /
• 2015

The quality of depth images is important in the 3D video system to represent complete 3D contents. However, the original depth image from a depth camera has a low resolution and a flickering problem which shows vibrating depth values in terms of temporal meaning. This problem causes an uncomfortable feeling when we look 3D contents. In order to solve a low resolution problem, we employ 3D warping and a depth weighted joint bilateral filter. A temporal mean filter can be applied to solve the flickering problem while we encounter a residual spectrum problem in the depth image. Thus, after classifying foreground andbackground regions, we use an upsampled depth image for a foreground region and temporal mean image for background region.Test results shows that the proposed method generates a time consistent depth video with a high resolution.