• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.552-556
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• 2016

Obstacle detection is a key feature in the safe driving control of electric wheelchairs. The suggested obstacle detection algorithm was designed to provide obstacle avoidance direction and detect the existence of cliffs. By means of this information, the wheelchair can determine where to steer and whether to stop or go. A 3D depth camera (Microsoft KINECT) is used to scan the 3D point data of the scene, extract information on obstacles, and produce a steering direction for obstacle avoidance. To be specific, ground detection is applied to extract the obstacle candidates from the scanned data and the candidates are projected onto a 2D map. The 2D map provides discretized information of the extracted obstacles to decide on the avoidance direction (left or right) of the wheelchair. As an additional function, cliff detection is developed. By defining the "cliffband," the ratio of the predefined band area and the detected area within the band area, the cliff detection algorithm can decide if a cliff is in front of the wheelchair. Vehicle tests were carried out by applying the algorithm to the electric wheelchair. Additionally, detailed functions of obstacle detection, such as providing avoidance direction and detecting the existence of cliffs, were demonstrated.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.808-811
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• 2020

This paper proposes a method to minimize the error of the Kinect camera sensor by using a random undirected Kalman filter. Kinect cameras, which provide RGB values and depth information, cause nonlinear errors in the sensor, causing problems in various applications such as skeleton detection. Conventional methods have tried to remove errors by using various filtering techniques. However, there is a limit to removing nonlinear noise effectively. Therefore, in this paper, a randomized unscented Kalman filter was applied to predict and update the nonlinear noise characteristics, we next tried to enhance a performance of skeleton detection. The experimental results confirmed that the proposed method is superior to the conventional method in quantitative results and reconstructed images on 3D space.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.74-83
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• 2019

This study designs a squat posture recognition system that can provide correct squat posture guidelines. This system comprises two modules: a Kinect camera for monitoring users' body movements and a Wii Balance Board(WBB) for measuring balanced postures with legs. Squat posture recognition involves two states: "Stand" and "Squat." Further, each state is divided into two postures: correct and incorrect. The incorrect postures of the Stand and Squat states were classified into three and two different types of postures, respectively. The factors that determine whether a posture is incorrect or correct include the difference between shoulder width and ankle width, knee angle, and coordinate of center of pressure(CoP). An expert and 10 participants participated in experiments, and the three factors used to determine the posture were measured using both Kinect and WBB. The acquired data from each device show that the expert's posture is more stable than that of the subjects. This data was classified using a support vector machine (SVM) and $na{\ddot{i}}ve$ Bayes classifier. The classification results showed that the accuracy achieved using the SVM and $na{\ddot{i}}ve$ Bayes classifier was 95.61% and 81.82%, respectively. Therefore, the developed system that used Kinect and WBB could classify correct and incorrect postures with high accuracy. Unlike in other studies, we obtained the spatial coordinates using Kinect and measured the length of the body. The balance of the body was measured using CoP coordinates obtained from the WBB, and meaningful results were obtained from the measured values. Finally, the developed system can help people analyze the squat posture easily and conveniently anywhere and can help present correct squat posture guidelines. By using this system, users can easily analyze the squat posture in daily life and suggest safe and accurate postures.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.130-139
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• 2021

In this study, size of the fruit of Japanese apricot (plum) was estimated through a plum recognition and size estimation program using 3D images in order to control the Eurytoma maslovskii that causes the most damage to plum in a timely manner. In 2018, night shooting was carried out using a Kinect 2.0 Camera. For night shooting in 2019, a RealSense Depth Camera D415 was used. Based on the acquired images, a plum recognition and estimation program consisting of four stages of image preprocessing, sizeable plum extraction, RGB and depth image matching and plum size estimation was implemented using MATLAB R2018a. The results obtained by running the program on 10 images produced an average plum recognition error rate of 61.9%, an average plum recognition error rate of 0.5% and an average size measurement error rate of 3.6%. The continued development of these plum recognition and size estimation programs is expected to enable accurate fruit size monitoring in the future and the development of timely control systems for Eurytoma maslovskii.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.405-417
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• 2020

Due to the popularization of the Extended Reality, research is actively underway to implement human motion in real-time 3D animation. In particular, Microsoft developed Kinect cameras for 3D motion information can be obtained without the burden of facilities and with simple operation, real-time animation can be generated by combining with 3D formats such as FBX. Compared to the marker-based motion capture system, however, Kinect has low accuracy due to its lack of estimated performance of joint information. In this paper, two algorithms are proposed to correct joint estimation errors in order to realize natural human motion in motion capture animation system in Kinect camera-based FBX format. First, obtain the position information of a person with a Kinect and create a depth map to correct the wrong joint position value using the human body segment length constraint information, and estimate the new rotation value. Second, the pre-set joint motion range constraint is applied to the existing and estimated rotation value and implemented in FBX to eliminate abnormal behavior. From the experiment, we found improvements in human behavior and compared errors between algorithms to demonstrate the superiority of the system.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.1-10
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• 2018

Aggression among pigs adversely affects economic returns and animal welfare in intensive pigsties. Recently, some studies have applied information technology to a livestock management system to minimize the damage resulting from such anomalies. Nonetheless, detecting each pig in a crowed pigsty is still challenging problem. In this paper, we propose a new Kinect camera and deep learning-based monitoring system for the detection of the individual pigs. The proposed system is characterized as follows. 1) The background subtraction method and depth-threshold are used to detect only standing-pigs in the Kinect-depth image. 2) The standing-pigs are detected by using YOLO (You Only Look Once) which is the fastest and most accurate model in deep learning algorithms. Our experimental results show that this method is effective for detecting individual pigs in real time in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (average 99.40% detection accuracies).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.12
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• pp.2297-2304
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• 2016

In this study, we designed and developed the virtual reality Exergame using the smart mat and the camera sensor for exercises in indoor environments. For detecting the gestures of a upper body of users, the KINECT camera based the gesture recognition algorithm used angles between user's joint information system was adopted, and the smart mat system including a LED equipment and Bluetooth communication module was developed for user's stepping data during the exercises that requires the gestures and stepping of users. Finally, the integrated virtual reality Exergame system was implement along with the Unity 3D engine and different kinds of user' virtual avatar characters with entertainment game contents such as displaying gesture guideline and a scoring function. Therefore, the designed system will useful for elders who need to improve cognitive ability and sense of balance or general users want to improve exercise ability and the indoor circumstances such home or wellness centers.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1056-1063
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• 2015

In this paper, we develop an interface for aerial photograph platform which consists of a quadrotor and a gimbal using the human body and the head posture. As quadrotors have been widely adopted in many industries such as aerial photography, remote surveillance, and maintenance of infrastructures, the demand of aerial video and photograph has been increasing remarkably. Stick type remote controllers are widely used to control a quadrotor, but this method is not an intuitive way of controlling the aerial vehicle and the camera simultaneously. Therefore, a new interface which controls the serial photograph platform is presented. The presented interface uses the human head movement measured by head-mounted display as a reference for controlling the camera angle, and the human body posture measured from Kinect for controlling the attitude of the quadrotor. As the image captured by the camera is displayed on the head-mounted display simultaneously, the user can feel flying experience and intuitively control the quadrotor and the camera. Finally, the performance of the developed system shown to verify the effectiveness and superiority of the presented interface.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.637-644
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• 2021

In general, in the fields of computer vision, robotics, and augmented reality, the importance of 3D space and 3D object detection and recognition technology has emerged. In particular, since it is possible to acquire RGB images and depth images in real time through an image sensor using Microsoft Kinect method, many changes have been made to object detection, tracking and recognition studies. In this paper, we propose a method to improve the quality of 3D reconstructed images by processing images acquired through a depth-based (RGB-Depth) camera on a multi-view camera system. In this paper, a method of removing noise outside an object by applying a mask acquired from a color image and a method of applying a combined filtering operation to obtain the difference in depth information between pixels inside the object is proposed. Through each experiment result, it was confirmed that the proposed method can effectively remove noise and improve the quality of 3D reconstructed image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.10
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• pp.920-927
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• 2013

Chroma key technique which composes images by separating an object from its background in specific color has restrictions on color and space. Especially, unlike general chroma key technique, image composition for stereo 3D display requires natural image composition method in 3D space. The thesis attempted to compose images in 3D space using depth keying method which uses high resolution depth information. High resolution depth map was obtained through camera calibration between the DSLR and Kinect sensor. 3D mesh model was created by the high resolution depth information and mapped with RGB color value. Object was converted into point cloud type in 3D space after separating it from its background according to depth information. The image in which 3D virtual background and object are composed obtained and played stereo 3D images using a virtual camera.