• Annual Conference of KIPS
• /
• 2018.10a
• /
• pp.885-888
• /
• 2018

This paper researches a way of detecting fish robots moving in an aquarium. The fish robot was designed and developed for interactions with humans in aquariums. It was studied merely to detect a moving object in an aquarium because we need to find the positions of moving fish robots. The intention is to recognize the location of robotic fish using an image processing technique and a video camera. This method is used to obtain the velocity for each pixel in an image, and assumes a constant velocity in each video frame to obtain positions of fish robots by comparing sequential video frames. By using this positional data, we compute the distance between fish robots using a mathematical expression, and determine which fish robot is leading and which one is lagging. Then, the lead robot will wait for the lagging robot until it reaches the lead robot. The process runs continuously. This system is exhibited in the Busan Science Museum, satisfying a performance test of this algorithm.

• Proceedings of the KIEE Conference
• /
• 2005.07b
• /
• pp.1702-1704
• /
• 2005

In solar power system, the height and azimuth of the sun are important parameters which control generated power magnitude. The way that controls the daily generation magnitude according to latitude and longitude and uses two axles is often used in the existing sunlight racing system now. In this two-axle sunlight track control system the self-load is concentrated on one FRAME. It is influenced of the regular load, snow load and the wind load, etc. It is difficult to set up the system in the building already built up. This research is a development about the small-scale economy track device of independent load-dispersing type solar generation system. The position track algorithm is through calculating the trail of height and azimuthal of the sun calculation to follow the sun.

• Journal of Power System Engineering
• /
• v.10 no.4
• /
• pp.139-146
• /
• 2006

In this paper vector code correlation(VCC) method and an algorithm to promote the image processing performance in building an effective measurement system using cameras are described for automatically berthing and controlling the ship equipped with side thrusters. In order to realize automatic ship berthing, it is indispensable that the berthing assistant system on the ship should continuously trace a target in the berth to measure the distance to the target and the ship attitude, such that we can make the ship move to the specified location. The considered system is made up of 4 apparatuses compounded from a CCD camera, a camera direction controller, a popular PC with a built in image processing board and a signal conversion unit connected to parallel port of the PC. The object of this paper is to reduce the image processing time so that the berthing system is able to ensure the safety schedule against risks during approaching to the berth. It could be achieved by composing the vector code image to utilize the gradient of an approximated plane found with the brightness of pixels forming a certain region in an image and verifying the effectiveness on a commonly used PC. From experimental results, it is clear that the proposed method can be applied to the measurement system for automatic ship berthing and has the image processing time of fourfold as compared with the typical template matching method.

• Journal of Korean Society of Transportation
• /
• v.19 no.6
• /
• pp.161-170
• /
• 2001

Vehicle detection is the basic of traffic monitoring. Video based systems have several apparent advantages compared with other kinds of systems. However, In video based systems, shadows make troubles for vehicle detection. especially active shadows resulted from moving vehicles. In this paper a new method that combines background subtraction and edge detection is proposed for vehicle detection and shadow rejection. The method is effective and the correct rate of vehicle detection is higher than 98(%) in experiments, during which the passive shadows resulted from roadside buildings grew considerably. Based on the proposed vehicle detection method, vehicle tracking, counting, classification and speed estimation are achieved so that traffic information concerning traffic flow is obtained to describe the load of each lane.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.24 no.1
• /
• pp.54-61
• /
• 2014

This paper proposes a driver supportive device with haptic cue function which can transmit optimal gear shifting timing to a driver without requiring the driver's visual attention. Its performance is evaluated under vehicle model considering automotive engine, transmission and vehicle body. In order to achieve this goal, a torque feedback device is devised and manufactured by adopting the MR (magnetorheological) fluid and clutch mechanism. The manufactured MR clutch is then integrated with the accelerator pedal to construct the proposed haptic cue device. A virtual vehicle emulating a four-cylinder four-stroke engine, manual transmission system of a passenger vehicle and vehicle body is constructed and communicated with the manufactured haptic cue device. Control performances including torque tracking and fuel efficiency are experimentally evaluated via a simple feed-forward control algorithm.

• 한국가시화정보학회:학술대회논문집
• /
• 2006.12a
• /
• pp.1-6
• /
• 2006

Single-Camera Stereoscopic Vision three-dimensional measurement system has been developed based upon 30-PTV algorithm. The system consists of one camera $(1k\times1k)$ and a host computer. To attain three-dimensional measurements a plate having stereo holes has been installed inside of the lens system. Three-dimensional measurements was successfully attained by adopting the conventional 30-PTV camera calibration methods. As applications of the constructed measurement system, a water droplet mixed with alcohol was constructed on a transparent plastic plate with the contacted fluid diameter 4mm, and the particles motions inside of the droplet have been investigated with the constructed measurement system. The measurement uncertainty of the constructed system was 0.04mm, 0.04mm and 0.09mm for X, Y and Z coordinates.

• Journal of Korea Multimedia Society
• /
• v.19 no.9
• /
• pp.1647-1658
• /
• 2016

RGB+D database has been widely used in object recognition, object tracking, robot control, to name a few. While rapid advance of active depth sensing technologies allows for the widespread of indoor RGB+D databases, there are only few outdoor RGB+D databases largely due to an inherent limitation of active depth cameras. In this paper, we propose a novel method used to build outdoor RGB+D databases. Instead of using active depth cameras such as Kinect or LIDAR, we acquire a pair of stereo image using high-resolution stereo camera and then obtain a depth map by applying stereo matching algorithm. To deal with estimation errors that inevitably exist in the depth map obtained from stereo matching methods, we develop an approach that estimates confidence of depth maps based on unsupervised learning. Unlike existing confidence estimation approaches, we explicitly consider a spatial correlation that may exist in the confidence map. Specifically, we focus on refining confidence feature with the assumption that the confidence feature and resultant confidence map are smoothly-varying in spatial domain and are highly correlated to each other. Experimental result shows that the proposed method outperforms existing confidence measure based approaches in various benchmark dataset.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.8 no.3
• /
• pp.185-191
• /
• 2008

In this paper, experimental studies of a neural network (NN) control technique for non-model based position control of the x-y table robot are presented. Decentralized neural networks are used to control each axis of the x-y table robot separately. For an each neural network compensator, an inverse control technique is used. The neural network control technique called the reference compensation technique (RCT) is conceptually different from the existing neural controllers in that the NN controller compensates for uncertainties in the dynamical system by modifying desired trajectories. The back-propagation learning algorithm is developed in a real time DSP board for on-line learning. Practical real time position control experiments are conducted on the x-y table robot. Experimental results of using neural networks show more excellent position tracking than that of when PD controllers are used only.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.79-79
• /
• 2015

현재 홍수 시 유량조사에 가장 많이 사용하고 있는 부자법은 측정 인력, 측정비용 및 위험성이 높다는 단점이 있다. 또한 교량에서 부자를 투하하고 측면에서 부자의 이동을 추적하기 때문에 평면상의 이동에 대한 정보를 얻기 어렵다는 한계가 있다. 이에 김서준 등(2014)은 PTV 기법을 이용한 부자 추적 알고리즘을 개발하였으나 부자가 회전하거나 물속에 잠기는 부분이 변화하여 수면 위로 확인되는 부자의 길이가 변할 경우 추적이 어렵다는 한계가 있었다. 이를 개선하고자 본 연구에서는 Template Match 알고리즘과 색상 기반 영상 처리 기법을 이용한 목표물 인식 방법인 Camshift 기법을 적용하여 부자를 추적할 수 있는 알고리즘을 개발하였다. Template Match 알고리즘의 경우는 입자가 많을수록 추적을 잘한다는 장점이 있지만 회전 및 변형에 취약하다는 단점이 있고, Camshift 영상 처리 기법의 경우 다수의 추적자가 존재할 경우 추적에 어려움이 있으나 추적자의 회전과 변형을 정확하게 추적할 수 있다는 장점이 있다. 따라서 Template Match 알고리즘을 이용하여 이동 예상영역을 결정하고 Camshift 영상 처리 기법으로 추적을 하게되면 두 방법의 장점을 모두 살릴 수 있다. Camshift 영상 처리 기법을 실제 부자 추적에 적용해 본 결과 부자의 회전 및 변형에도 정확하게 추적할 수 있는 것을 확인하였다. 향후 부자법을 이용한 유량 조사에 본 연구에서 개발한 알고리즘을 적용한다면 현장에서 동영상 촬영만 하면 되기 때문에 측정 인원을 최소화 할 수 있어 매우 경제적이고, 홍수 시 위험성도 감소할 것으로 기대된다.

• International Journal of Internet, Broadcasting and Communication
• /
• v.11 no.4
• /
• pp.76-85
• /
• 2019

We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose sub-frame analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed forfull frame image. We reduced its computationalrequirementsignificantly without losing throughput and object detection accuracy with the proposed method.