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.

In these days, mobile technology such as smart phone and GPS have an effect on business processes of many companies especially a transportation company. The purpose of this paper is to present the development processes of real time vehicles tracking and intelligent TMS(Transportation Management System) using smart phone applications. The objective of this study is two-fold. The first is to redesign business process of the transportation company. Using BPR(business process re-engineering), we analyze current processes to find opportunities for improvement redefining processes after adopting mobile technology precisely. The second is to develop the real time vehicles tracking and intelligent TMS. Proposed system consists of four parts: (1) intelligent TMS(web system) (2) real time vehicle tracking application for TMS (3) real time tracking application for customer (4) salesman supporting application. Developed system was tested at the transportation company and was found to be useful system.

영상처리시스템(VIPS: Video Image Processing System)은 실시간으로 들어오는 영상정보를 분석하여 유용한 정보를 제공하며, 하나의 카메라로 여러 차로를 동시에 감시할 수 있는 알고리즘으로 교통량, 속도뿐만 아니라 밀도 및 점유율 등 다양한 정보를 제공한다. 영상검지시스템으로 상용화 제품은 Tripwire시스템으로 검지영역의 픽셀 변화량으로 차량검지를 하나, 이는 교통량, 속도 등 단편적인 정보에 국한될 수 밖에 없다. 반면, 영상검지시스템이 개별차량에 대한 추적시스템으로 개발할 경우 사고 및 차로 변경의 위험요소 감지 등 보다 다양한 정보를 제공할 수가 있다. 본 논문은 컴퓨터비전 기술을 이용하여 Tripwire에서 수집할 수 있는 교통정보와 동일한 정보를 제공하는 개별차량의 추적시스템을 개발하였으며 이 시스템을 실제 도로영상에 적용하여 상용화된 시스템과 결과를 비교함으로써 성능검증을 하였다.

This paper describes on the consolidation of AIS and ARPA radar positions by comparing the AIS and ARPA radar information for the tracked ship targets using a PC-based ECDIS in Busan harbor, Korea. The information of AIS and ARPA radar target was acquired independently, and the tracking parameters such as ship's position, COG, SOG, gyro heading, rate of turn, CPA, TCPA, ship s name and MMSI etc. were displayed automatically on the chart of a PC-based ECDIS with radar overlay and ARPA tracking. The ARPA tracking information obtained from the observed radar images of the target ship was compared with the AIS information received from the same vessel to investigate the difference in the position and movement behavior between AIS and ARPA tracked target ships. For the ARPA radar and AIS targets to be consolidated, the differences in range, speed, course, bearing and distance between their targets were estimated to obtain a clear standards for the consolidation of ARPA radar and AIS targets. The average differences between their ranges, their speeds and their courses were 2.06% of the average range, -0.11 knots with the averaged SOG of 11.62 knots, and 0.02˚ with the averaged COG of 37.2˚, respectively. The average differences between their bearings and between their positions were -1.29˚ and 68.8m, respectively. From these results, we concluded that if the ROT, COG, SOG, and HDG informations are correct, the AIS system can be improved the prediction of a target ship's path and the OOW(Officer of Watch) s ability to anticipate a traffic situation more accurately.

This paper describes on the real-time tracking of ship's dynamic behavior by AIS information in the coastal waters. The AIS data was received at a land station by using the antenna of AIS receiver mounted on the rooftop of the laboratory, Pukyong National University (PKNU), Busan, Korea, and stored as a NMEA format of serial output sentence of VDM(VHF Data-Link Message) and displayed on the ENC(Electronic Navigational Chart) of a PC-based ECDIS. In this study, the AIS receiver was mainly used to obtain the dynamic information that is necessary to evaluate and track the movement situation of training ship "KAYA" of PKNU in the coastal waters. The change of position with time for the ship turning under the rudder angle of port 30˚ was correctly tracked with the turning circle of 940 m in diameter on the ENC of a PC-based ECDIS. Then, the dynamic information of the AIS system was updated every 6.29 seconds under the turning situation for the speed of 10.9 knots and every 21.65 seconds under the situation running at the speed of 11.05 knots on the straight line route of 155˚, respectively. In case of AIS target tracking in the inshore zone behind large topographical obstructions, such as mountain and apartment buildings, the update rate of dynamic information was irregularly changed by the existence of land obstacles. However, the position tracking by AIS information under the situation existing no sea obstructions was achieved in real or near real-time and the instant presentation of course alternations for the ship was correctly monitored by using a PC-based ECDIS. From these results, we concluded that the PC-based ECDIS technology and methodology combined with the AIS information can be easily extended and applied to the surveillance and management for the fishing operation of fishing vessels in the coastal zone and in the EEZ fishing grounds.

레이더 정보의 수록 및 정량적 해석을 위한 시도로써 타선의 위치정보를 자선의 전자해도 및 radar 화변상에 모니터링하여 두 선박의 간격을 측정할 수 있는 양선거리측정시스템과 ARPA radar가 탐지한 표적의 추적정보를 자선의 전자해도 화면상에 실시간으로 모니터링하기 위한 시스템을 구축함과 동시에 이들 레이더 정보를 필요에 따라 수록 및 재생할 수 있는 레이더 정보수록 및 해석 시스템을 개발하고, 실용화 실험을 행한 결과를 요약하면 다음과 같다. (1) 목포 인근해역에서 레이더 신호를 수록하고, 이 레이더 영상에 위치발생시뮬레이터에 의해 생성한 타선 위치를 RS232C interface를 통해 전송 및 중첩시켜 타선의 위치를 추적하면서 실시간으로 본선과 타선의 양선 간격을 산출한 결과, 양선 거리 및 방위의 실시간 측정이 가능하여 이 시스템을 양선 거리계로써 활용할 수 있음이 입증되었다. (2) 타선의 레이더 신호를 수신한 후, α-β tracker를 이용하여 타선 영상의 중심 위치를 실시간으로 추적하면서 침로, 속력, 방위, 거리 등을 예측한 결과, 매우 안정된 평활화 추정치를 얻을 수 있었다. (3) ARPA 시뮬레이터를 이용하여 표적의 추적정보를 TTM sentence 등으로써 생성한 후, 이 코드를 전자해도상에 전송 및 중첩 표시시킨 결과, 추적표적의 위치, 속력, 침로, 방위, 거리 등의 추적정보의 실시간 모니터링이 안정적으로 실현된 바, 선내의 여러 장소에서 ARPA 정보를 공유하기 위한 멀티 모니터링 장치의 개발이 기대된다.

This research is a case study of underwater object tracking based on real-time recurrent regression networks (Re3). Re3 has the concept of generic object tracking. Because of these characteristics, it is very effective to apply this model to unclear underwater sonar images. The model also an pursues object tracking method, thus it solves the problem of calculating load that may be limited when object detection models are used, unlike the tracking models. The model is also highly intuitive, so it has excellent continuity of tracking even if the object being tracked temporarily becomes partially occluded or faded. There are 4 types of the dataset using multi-beam sonar images: including (a) dummy object floated at the testbed; (b) dummy object settled at the bottom of the sea; (c) tire object settled at the bottom of the testbed; (d) multi-objects settled at the bottom of the testbed. For this study, the experiments were conducted to obtain underwater sonar images from the sea and underwater testbed, and the validity of using noisy underwater sonar images was tested to be able to track objects robustly.

The global trends of shorter delivery times and the safety of important payload in production networks are leading to higher synchronization efforts between production and delivery processes. By now, research activities in intelligent shipment are expanding quickly in the case of possibilities and importance of usage, which means payload that can identify, monitor or locate itself. In this study, it is proposed that new generation system for continuously monitoring payloads during delivery; real-time monitoring of truck loading states; a new improved algorithm for intelligent monitoring of delivery processing; the possibility of a detailed analysis of the truck loading states in real-time and payload safety; and more efficient truck tracking.

Facial feature extraction and tracking are essential steps in human-robot-interaction (HRI) field such as face recognition, gaze estimation, and emotion recognition. Active shape model (ASM) is one of the successful generative models that extract the facial features. However, applying only ASM is not adequate for modeling a face in actual applications, because positions of facial features are unstably extracted due to limitation of the number of iterations in the ASM fitting algorithm. The unaccurate positions of facial features decrease the performance of the emotion recognition. In this paper, we propose real-time facial feature extraction and tracking framework using ASM and LK optical flow for emotion recognition. LK optical flow is desirable to estimate time-varying geometric parameters in sequential face images. In addition, we introduce a straightforward method to avoid tracking failure caused by partial occlusions that can be a serious problem for tracking based algorithm. Emotion recognition experiments with k-NN and SVM classifier shows over 95% classification accuracy for three emotions: "joy", "anger", and "disgust".