It is important to ensure worker’s safety from radiation hazard in decommissioning site. Real-time tracking of worker’s location is one of the factors necessary to detect radiation hazard in advance. In this study, the integrated algorithm for worker tracking has been developed to ensure the safety of workers. There are three essential techniques needed to track worker’s location, which are object detection, object tracking, and estimating location (stereo vision). Above all, object detection performance is most important factor in this study because the performance of tracking and estimating location is depended on worker detection level. YOLO (You Only Look Once version 5) model capable of real-time object detection was applied for worker detection. Among the various YOLO models, a model specialized for person detection was considered to maximize performance. This model showed good performance for distinguishing and detecting workers in various occlusion situations that are difficult to detect correctly. Deep SORT (Simple Online and Realtime Tracking) algorithm which uses deep learning technique has been considered for object tracking. Deep SORT is an algorithm that supplements the existing SORT method by utilizing the appearance information based on deep learning. It showed good tracking performance in the various occlusion situations. The last step is to estimate worker’s location (x-y-z coordinates). The stereo vision technique has been considered to estimate location. It predicts xyz location using two images obtained from stereo camera like human eyes. Two images are obtained from stereo camera and these images are rectified based on camera calibration information in the integrated algorithm. And then workers are detected from the two rectified images and the Deep SORT tracks workers based on worker’s position and appearance between previous frames and current frames. Two points of workers having same ID in two rectified images give xzy information by calculating depth estimation of stereo vision. The integrated algorithm developed in this study showed sufficient possibility to track workers in real time. It also showed fast speed to enable real-time application, showing about 0.08 sec per two frames to detect workers on a laptop with high-performance GPU (RTX 3080 laptop version). Therefore, it is expected that this algorithm can be sufficiently used to track workers in real decommissioning site by performing additional parameter optimization.

More than 6,000 power tiller accidents occurred in 2015, accounting for 50% of all agricultural machinery accidents. Despite this, educational institutions for farmers are only conducting theoretical education due to lack of training systems with guaranteed safety. This study developed an object motion tracking algorithm enabling trainees to control a power tiller driving simulator while wearing a HMD(head mounted display) in order to provide safe hands-on training equipment. A power tiller driving simulator was built using encoders, proximity sensors and displacement sensors to detect the locations of various operating devices such as steering clutch, and a computer model for this simulator was designed. Center coordinate synchronization of the driving simulator and the computer model was achieved with a tracker, and the motion of the power tiller driving simulator was tracked by computing position coordinates and rotation angles of the simulator. The maximum distance error was 23mm, and there was no difficulty maneuvering the driving simulator while wearing an HMD, even at maximum distance error. This motion tracking algorithm is expected to be applicable to the development of mixed reality based power tiller driving simulators for training, contributing to the reduction of power tiller accidents.

사용자의 수요가 증가함으로 인하여 최근의 마커 기반 증강현실 기술은 제스처 기반 인간-컴퓨터 상호작용 분야에서 주목을 받고 있다. 그러나 개체가 프레임에서 빠른 움직임을 보일 때 발생하는 모션 블러 효과에 의하여 마커의 추적 및 감지의 한계점이 발생한다. 기존의 디블러링 기술들에서 전체 프레임 중 특정 프레임을 추출하는 작업이 없다면 실시간 비디오에서 사용하기에 문제가 있다. 본 논문에서는 인간-컴퓨터 상호작용을 위하여 ArUco 마커를 사용한 특징점 기반 광학흐름 추적 방법을 제안하며, 마커를 이용한 자세 추정 방법을 설명한다. 이 방법은 ArUco 마커를 감지하고 특수한 마커 추적 방법을 통해 마커 감지를 보완한다. 특히 마커 추적 방법은 FAST 알고리즘을 사용하여 특징점을 추출하고 루카스-카나데 방법을 사용하여 특징점의 움직임을 분석하여 움직임 벡터를 계산한다. 또한 Perspective-n-Point 문제를 해결하여 마커 포즈 추정을 구현했다. 제안된 시스템은 기존의 방법보다 높은 검출률을 보였으며, 마커를 포함한 비디오 에서 프레임 처리 속도가 약 37% 향상되었다. 또한 마커 포즈 추정을 그래픽으로 구현하였다. 이 연구는 실제 환경에서 카메라를 통한 제스처 기반 인간-컴퓨터 상호작용 분야와 또한 이동 로봇 분야에도 도움이 될 것이라 기대된다.

For several years, keyboard and mouse have been used as the main interacting devices between users and computer games, but they are becoming outdated. Gesture-based human-computer interaction systems are becoming more popular owing to the emergence of virtual reality and augmented reality technologies. Therefore research on these systems has attracted a significant attention. The researches focus on designing the interactive interfaces between users and computers. Human-computer interaction is an important factor in computer games because it affects not only the experience of the users, but also the design of the entire game. In this research, we develop an particle filter-based face tracking method using color distributions as features, for the purpose of applying to gesture-based human-computer interaction systems for computer games. The experimental results proved the efficiency of particle filter and color features in face tracking, showing its potential in designing human-computer interactive games.

자동사고검지 알고리즘의 대부분은 사고가 발생했을 때 사고로 검지하지 못하고, 혼잡으로 검지하는 경우가 많다는 문제점을 가지고 있다. 또한 교통정보센터 운영자들은 교통사고검지시스템을 운영하면서 대부분 CCTV 육안감시 또는 운전자들의 신고에 의존하여 사고처리를 하고 있는 실정이다. 그 이유는 현재 운영되고 있는 교통사고검지시스템에서는 실제 사고가 아닌데도 불구하고, 사고라는 오검지 경고가 많이 발생되어 시스템 전체의 신뢰도가 떨어진다는 문제점이 있기 때문이다. 다시 말해 교통사고검지시스템의 알고리즘은 검지율(Detection probability)이 높아야 함과 동시에, 오검지율(False alarm probability)은 낮아야 하고, 정확한 사고지점과 시간을 검지해 낼 수 있어야 한다. 이에 본 연구는 검지율을 높이고 동시에, 오검지율을 낮추는 방법으로 기 개발된 가우시안 혼합모델(Gaussian Mixture Model)과 개별차량 Tracking을 이용하여 개발한 사고검지 알고리즘을 교통정보센터 관리시스템(Center Management System)에 적용하고, 실제 교통상황에서 사고검지율과 오검지의 빈도를 측정하여 그 효과를 검증 및 평가하고자 한다.

국제해사기구(IMO)는 해상인명안전협약(SOLAS)에 2002년 7월 1일부터 신조되는 총톤수 500톤 이상의 모든 선박에 자동선박추적장치(ATA)를 탑재시키도록 규정하였으나, 이SOLAS 협약에 적용되지 않는 현재 운항중인 10,000톤 미만의 현존선은 ATA의 비탑재로 인하여 충돌사고가 빈발하고 있다. 본 논문에서는 ATA의 일부 요소기술이 국산화되어 있으나 가장 핵심적인 자동추적 알고리즘 개발은 아직 미비한 실정에 있으므로 자동추적 알고리즘의 핵심요소기술의 연구개발을 통하여 ATA를 국산화하여 연안에서 항해하는 중소형 선박에 보급함으로서 충돌사고 둥을 미연에 방지하고자 한다.

In a four-wheel independent drive platform, four wheels and motors are connected directly, and the rotation of the motors generates the power of the platform. It uses a skid steering system that steers based on the difference in rotational power between wheel motors. The platform can control the speed of each wheel individually and has excellent mobility on dirt roads. However, the difficulty of the straight-running is caused due to torque distribution variation in each wheel’s motor, and the direction of rotation of the wheel, and moving direction of the platform, and the difference of the platform’s target direction. This paper describes an algorithm to detect the slip generated on each wheel when a four-wheel independent drive platform is traveling in a harsh environment. When the slip is detected, a compensation control algorithm is activated to compensate the torque of the motor mounted on the platform to improve the trajectory tracking performance of the platform. The four-wheel independent drive platform developed for this study verified the algorithm. The wheel slip detection and the compensation control algorithm of the platform are expected to improve the stability of trajectory tracking.

We have developed an algorithm for tracking coronal mass ejection (CME) propagation that allows us to estimate CME speed and its arrival time at Earth. The algorithm may be used either to forecast the CME’s arrival on the day of the forecast or to update the CME tracking information for the next day’s forecast. In our case study, we successfully tracked CME propagation using the algorithm based on g-values of interplanetary scintillation (IPS) observation provided by the Institute for Space- Earth Environmental Research (ISEE). We were able to forecast the arrival time (Δt = 0.30 h) and speed (Δv = 20 km/s) of a CME event on October 2, 2000. From the CME-interplanetary CME (ICME) pairs provided by Cane & Richardson (2003), we selected 50 events to evaluate the algorithm’s forecast capability. Average errors for arrival time and speed were 11.14 h and 310 km/s, respectively. Results demonstrated that g-values obtained continuously from any single station observation were able to be used as a proxy for CME speed. Therefore, our algorithm may give stable daily forecasts of CME position and speed during propagation in the region of 0.2–1 AU using the IPS g-values, even if IPS velocity observations are insufficient. We expect that this algorithm may be widely accepted for use in space weather forecasting in the near future.

In this paper we propose the method that detects moving objects in autonomous navigation vehicle using LRF sensor data. Object detection and tracking methods are widely used in research area like safe-driving, safe-navigation of the autonomous vehicle. The proposed method consists of three steps: data segmentation, mobility classification and object tracking. In order to make the raw LRF sensor data to be useful, Occupancy grid is generated and the raw data is segmented according to its appearance. For classifying whether the object is moving or static, trajectory patterns are analysed. As the last step, Markov chain Monte Carlo (MCMC) method is used for tracking the object. Experimental results indicate that the proposed method can accurately detect moving objects.

워터젯이 탑재된 RIB(Rigid Inflatable Boat)형태의 무인수상선을 인한 경유점 추적 제어 알고리즘을 설계하였고, 성능 검증을 위해 실해역 시험을 수행하였다. 본 연구에서 사용된 RIB형 무인수상선의 경유점 추적제어를 위해서는 방향제어를 위해 버킷각을 제어하여야 한다. 우선, 육상 관제소에 미리 입력된 경유점들의 위경도 등의 위치정보들을 바탕으로, 목표 방향각을 실시간 계산한다. 그리고, 무인수상선에 탑재된 마그네틱 콤파스 등의 센서로부터 받은 선수각 및 선수각속도의 값과 PD 제어기법을 이용하여, 버킷각 명령을 실시간 계산한다. 본 연구에서는, 바람 등의 외력으로 인한 표류각을 보정하기 위해 일정속도 이상에서는 실침로(Course Of Ground, COG)를 사용하였다. 또한, 설계된 경유점 추적 제어 알고리즘을 검증하기 위해 부산 광안대교 근처 해역에서 육상관제소를 설치하고, 실선 시험을 수행하였다. 본 논문에서는, 설계된 무인 경유점 추적 제어 알고리즘의 시험결과를, 유인으로 제어한 결과 및 상용추적제어기로 제어한 결과들과 비교 분석하였다.

A narrow loop noise bandwidth method is desirable to reduce the error of raw measurements due to the thermal noise. However, it degrades the performance of GPS initial synchronization such as mean acquisition time. And it restricts the loop noise bandwidth to a fixed value determined by the lower bound of the allowable range of carrier-to-noise power ratio, so that it is difficult to optimally track GPS signal. In order to make up for the weak points of the fixed-type narrow loop noise bandwidth method and simultaneously minimize the error of code and carrier measurements, this paper proposes a stepwise-type adaptive bandwidth algorithm for DGPS reference receivers. In this paper, it is shown that the proposed adaptive bandwidth algorithm can provide more accurate measurements than those of the fixed-type narrow loop noise bandwidth method, in view of analyzing the simulation results between two signal tracking algorithms. This paper also carries out sensitivity analysis of the proposed adaptive bandwidth algorithm due to the estimation uncertainty of carrier-to-noise power ratio. Finally the analysis results are verified by the experiment using GPS simulator.

In this paper, a tracking algorithm for autonomous navigation of automated guided vehicles (AGVs) operating in container terminals is presented. The developed navigation algorithm takes the form of a federated information filter used to detect other AGVs and avoid obstacles using fused information from multiple sensors. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. It is proved that the information state and the information matrix of the suggested filter, which are weighted in terms of an information sharing factor, are equal to those of a centralized information filter under the regular conditions. Numerical examples using Monte Carlo simulation are provided to compare the centralized information filter and the proposed one.

A purpose of developing a sound source tracking system in this paper is to reduce the noise efficiently from the received signal by microphone array and measure the signal's time delay between the microphones. I have applied the wavelet analysis algorithm to the system and calculated the sound source's relative position For the performance evaluation, I have compared with the results of utilizing the digital filtering methods based on the FIR LPF using Kaiser window function and the inverse Chebyshev IIR LPF. As a result, I have confirmed the fact that 'time-scale' filter using inverse discrete wavelet transform was suitable for this system.