• Proceedings of the Korean Society of Computer Information Conference
• /
• 2019.01a
• /
• pp.109-110
• /
• 2019

본 논문에서는 Kinect sensor의 음성 인식 기능을 활용한 매장 주문 어플리케이션을 설계하고 구현한다. 노년층과 유아들은 무인주문결제시스템의 터치스크린 조작 및 사용자 인터페이스에 익숙하지 않기 때문에 매장에서 상품을 주문하고 결제하는데 불편함을 경험하고 있다. 이러한 불편함을 제거하기 위한 하나의 방법으로 키넥트 센서의 음성 인식 기능을 이용하여 음성으로 쉽게 주문을 할 수 있는 매장 주문 어플리케이션을 개발한다. Kinect에는 4개의 오디오 마이크로폰이 있어서 $-50^{\circ}{\sim}50^{\circ}$ 범위의 음성을 인식할 수 있으며 20db 이하의 주위 소음은 무시한다. 이 범위내에서 $10^{\circ}$씩 증가시키면서 음원의 위치를 추적한다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.6
• /
• pp.1183-1188
• /
• 2016

Kinect sensor version 2 is a kind of camera released by Microsoft as a computer vision and a natural user interface for game consoles like Xbox one. It allows acquiring color images, depth images, audio input and skeletal data with a high frame rate. In this paper, using depth image, we present a surveillance system of a certain area within Kinect's field of view. With computer vision library(Emgu CV), if an object is detected in the target area, it is tracked and kinect camera takes RGB image to send it in database server. Therefore, a mobile application on android platform was developed in order to notify the user that Kinect has sensed strange motion in the target region and display the RGB image of the scene. User gets the notification in real-time to react in the best way in the case of valuable things in monitored area or other cases related to a reserved zone.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.20 no.2
• /
• pp.205-213
• /
• 2018

Development and validation of crop models often require measurements of biomass for the crop of interest. Considerable efforts would be needed to obtain a reasonable amount of biomass data because the destructive sampling of a given crop is usually used. The Kinect sensor, which has a combination of image and depth sensors, can be used for estimating crop biomass without using destructive sampling approach. This approach could provide more data sets for model development and validation. The objective of this study was to examine the applicability of the Kinect sensor for estimation of chinese cabbage fresh weight. The fresh weight of five chinese cabbage was measured and compared with estimates using the Kinect sensor. The estimates were obtained by scanning individual chinese cabbage to create point cloud, removing noise, and building a three dimensional model with a set of free software. It was found that the 3D model created using the Kinect sensor explained about 98.7% of variation in fresh weight of chinese cabbage. Furthermore, the correlation coefficient between estimates and measurements were highly significant, which suggested that the Kinect sensor would be applicable to estimation of fresh weight for chinese cabbage. Our results demonstrated that a depth sensor allows for a non-destructive sampling approach, which enables to collect observation data for crop fresh weight over time. This would help development and validation of a crop model using a large number of reliable data sets, which merits further studies on application of various depth sensors to crop dry weight measurements.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.13 no.6
• /
• pp.237-242
• /
• 2013

There are some limitations to use the fitness center frequently by various reasons, excessive business workload, geographic location and time limit. This research specially focuses on the developement of health care application tool for aerobic/anaerobic exercise in a house accompanied by training coach, simply for the purpose of preventing from a boredom moment by exercing alone. This study newly proposes the realization of program tool by using Microsoft Kinect hardware tool which can be seen easily in the internet stores and can recognize the user. In addition, this program are designed to take care of movement control, weight and calory by maping the perceived user implementing modeling of 3D model.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.11
• /
• pp.2727-2732
• /
• 2014

Recently, the researches of object tracking and recognition using Microsoft's Kinect are being actively studied. In this environment human hand detection and tracking is the most basic technique for human computer interaction. This paper proposes a method of improving the accuracy of the detected hand region's boundary in the cluttered background. To do this, we combine the hand detection results using the skin color with the extracted depth image from Kinect. From the experimental results, we show that the proposed method increase the accuracy of the hand region detection than the method of detecting a hand region with a depth image only. If the proposed method is applied to the sign language or gesture recognition system it is expected to contribute much to accuracy improvement.

• Proceedings of the Korea Multimedia Society Conference
• /
• 2012.05a
• /
• pp.306-307
• /
• 2012

최근 키넥트 열풍을 반영하여 어린이들이 실내에서 신체 활동을 재미있게 학습할 수 있는 그리기 프로그램을 개발하였다. 20개의 인체 관절 좌표를 자동으로 실시간으로 사용자의 움직임을 추적하여 모션을 인식하고 해당하는 그리기 효과를 적용한다. 주요 기능은 메인 화면에서 다양한 펜 색깔과 스탬프, 페인트 볼 효과, 액자 그리기, 액션 명령어를 실행하는 것으로 구성되어 있다.

• ETRI Journal
• /
• v.35 no.6
• /
• pp.949-959
• /
• 2013

Since the recent launch of Microsoft Xbox Kinect, research on 3D human pose estimation has attracted a lot of attention in the computer vision community. Kinect shows impressive estimation accuracy and real-time performance on massive graphics processing unit hardware. In this paper, we focus on further reducing the computation complexity of the existing state-of-the-art method to make the real-time 3D human pose estimation functionality applicable to devices with lower computing power. As a result, we propose two simple approaches to speed up the random-forest-based human pose estimation method. In the original algorithm, the random forest classifier is applied to all pixels of the segmented human depth image. We first use a multi-scale approach to reduce the number of such calculations. Second, the complexity of the random forest classification itself is decreased by the proposed cascade approach. Experiment results for real data show that our method is effective and works in real time (30 fps) without any parallelization efforts.

• ETRI Journal
• /
• v.39 no.2
• /
• pp.181-190
• /
• 2017

The movements of the human body are difficult to capture owing to the complexity of the three-dimensional skeleton model and occlusion problems. In this paper, we propose a motion capture system that tracks dynamic human motions in real time. Without using external markers, the proposed system adopts multiple depth sensors (Microsoft Kinect) to overcome the occlusion and body rotation problems. To combine the joint data retrieved from the multiple sensors, our calibration process samples a point cloud from depth images and unifies the coordinate systems in point clouds into a single coordinate system via the iterative closest point method. Using noisy skeletal data from sensors, a posture reconstruction method is introduced to estimate the optimal joint positions for consistent motion generation. Based on the high tracking accuracy of the proposed system, we demonstrate that our system is applicable to various motion-based training programs in dance and Taekwondo.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2015.07a
• /
• pp.262-263
• /
• 2015

본 논문에서는 마이크로소프트 키넥트 센서를 이용한 실시간 성능의 3차원 환경 복원 알고리즘을 소개한다. 기존의 멀티키넥트 시스템을 확장하여 총 열두대의 키넥트를 사용하는데, 키넥트의 높은 대역폭 문제를 해결하기 위하여 키넥트가 여섯 대씩 연결된 두 대의 데스크탑을 UDP 통신으로 연결하였고, 각 키넥트로부터 들어오는 3차원 포인트클라우드로부터 확률적인 3차원 환경복원을 하기 위하여 옥토맵 알고리즘을 차용하였다. 또한, GPU를 연산에 활용함으로써 실시간 성능을 확보하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.05a
• /
• pp.279-282
• /
• 2017

Kinect depth sensor, a depth camera developed by Microsoft as a natural user interface for game appeared as a very useful tool in computer vision field. In this paper, due to kinect's depth sensor and its high frame rate, we developed a distance measurement system using Kinect camera to test it for unmanned vehicles which need vision systems to perceive the surrounding environment like human do in order to detect objects in their path. Therefore, kinect depth sensor is used to detect objects in its field of view and enhance the distance measurement system from objects to the vision sensor. Detected object is identified in accuracy way to determine if it is a real object or a pixel nose to reduce the processing time by ignoring pixels which are not a part of a real object. Using depth segmentation techniques along with Open CV library for image processing, we can identify present objects within Kinect camera's field of view and measure the distance from them to the sensor. Tests show promising results that this system can be used as well for autonomous vehicles equipped with low-cost range sensor, Kinect camera, for further processing depending on the application type when they reach a certain distance far from detected objects.