• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.18 no.2
• /
• pp.16-22
• /
• 2010

This paper proposes the real-time moving object tracking system UAV using color information. Case of object tracking, it have studied to recognizing the moving object or moving multiple objects on the fixed camera. And it has recognized the object in the complex background environment. But, this paper implements the moving object tracking system using the pan/tilt function of the camera after the object's region extraction. To do this tracking system, firstly, it detects the moving object of RGB/HSI color model and obtains the object coordination in acquired image using the compact boundary box. Secondly, the camera origin coordination aligns to object's top&left coordination in compact boundary box. And it tracks the moving object using the pan/tilt function of camera. It is implemented by the Labview 8.6 and NI Vision Builder AI of National Instrument co. It shows the good performance of camera trace in laboratory environment.

• Journal of Institute of Control, Robotics and Systems
• /
• v.15 no.1
• /
• pp.54-60
• /
• 2009

In this paper, a precision landing approach is implemented based on real-time image processing. A full-scale landmark for automatic landing is used. canny edge detection method is applied to identify the outside quadrilateral while circular hough transform is used for the recognition of inside circle. Position information on the ground landmark is uplinked to the unmanned helicopter via ground control computer in real time so that the unmanned helicopter control the air vehicle for accurate landing approach. Ground test and a couple of flight tests for autonomous landing approach show that the image processing and automatic landing operation system have good performance for the landing approach phase at the altitude of $20m{\sim}1m$ above ground level.

• Journal of Drive and Control
• /
• v.19 no.3
• /
• pp.16-22
• /
• 2022

Crushers are equipment that crush natural stones, to produce aggregates used at construction sites. As the crusher proceeds, the inner liner becomes worn, causing the size of the aggregate produced to gradually increase. The vision sensor-based aggregate analysis system analyzes the size and distribution of aggregates in production, in real time through image analysis. This study developed an algorithm that can segmentate aggregates in images in real time. using image preprocessing technology combining various filters and morphology techniques, and aggregate region characteristics such as convex hull and concave hull. We applied the developed algorithm to fine aggregate, intermediate aggregate, and thick aggregate images to verify their performance.

• Proceedings of the Korea Society of Information Technology Applications Conference
• /
• 2005.11a
• /
• pp.285-288
• /
• 2005

We propose a novel programmable miniature vision module based on a custom designed analog VLSI (aVLSI) chip. The vision module consists of the optical flow vision sensor embedded with commercial off-the-shelves digital hardware; in our case is the Intel XScale PXA270 processor enforced with a programmable gate array device. The aVLSI sensor provides gray-scale imager data as well as smooth optical flow estimates, thus each pixel gives a triplet of information that can be continuously read out as three independent images. The particular computational architecture of the custom designed sensor, which is fully parallel and also analog, allows for efficient real-time estimations of the smooth optical flow. The Intel XScale PXA270 controls the sensor read-out and furthermore allows, together with the programmable gate array, for additional higher level processing of the intensity image and optical flow data. It also provides the necessary standard interface such that the module can be easily programmed and integrated into different vision systems, or even form a complete stand-alone vision system itself. The low power consumption, small size and flexible interface of the proposed vision module suggests that it could be particularly well suited as a vision system in an autonomous robotics platform and especially well suited for educational projects in the robotic sciences.

• Korean Journal of Remote Sensing
• /
• v.23 no.5
• /
• pp.365-373
• /
• 2007

Car navigation system is a key application in geographic information system and telematics. A recent trend of car navigation system is using real video captured by camera equipped on the vehicle, because video has more representation power about real world than conventional map. In this paper, we suggest a visual car navigation system that visually represents route guidance. It can improve drivers' understanding about real world by capturing real-time video and displaying navigation information overlaid directly on the video. The system integrates real-time data acquisition, conventional route finding and guidance, computer vision, and augmented reality display. We also designed visual navigation controller, which controls other modules and dynamically determines visual representation methods of navigation information according to current location and driving circumstances. We briefly show implementation of the system.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.2
• /
• pp.20-25
• /
• 2021

The target-object classification method was implemented using a deep-learning-based detection model in real-time images. The object detection model was a deep-learning-based detection model that allowed extensive data collection and machine learning processes to classify similar target-objects. The recognition model was implemented by changing the processing structure of the detection model and combining developed the vision-processing module. To classify the target-objects, the identity and similarity were defined and applied to the detection model. The use of the recognition model in industry was also considered by verifying the effectiveness of the recognition model using the real-time images of an actual soccer game. The detection model and the newly constructed recognition model were compared and verified using real-time images. Furthermore, research was conducted to optimize the recognition model in a real-time environment.

• Journal of KIISE:Software and Applications
• /
• v.37 no.8
• /
• pp.641-646
• /
• 2010

This paper presents algorithms for real-time navigation of a humanoid robot with a stereo vision but no other sensors. Using the algorithms, a robot can recognize its 3D environment by retrieving SIFT features from images, estimate its position through the Kalman filter, and plan its path to reach a destination avoiding obstacles. Our approach focuses on estimating the robot’s central walking path trajectory rather than its actual walking motion by using an approximate model. This strategy makes it possible to apply mobile robot localization approaches to humanoid robot localization. Simple collision free path planning and motion control enable the autonomous robot navigation. Experimental results demonstrate the feasibility of our approach.

• Journal of Korea Multimedia Society
• /
• v.16 no.12
• /
• pp.1393-1402
• /
• 2013

Hand gesture is one of the most popular communication methods in everyday life. In human-computer interaction applications, hand gesture recognition provides a natural way of communication between humans and computers. There are mainly two methods of hand gesture recognition: glove-based method and vision-based method. In this paper, we propose a vision-based hand gesture recognition method using Kinect. By using the depth information is efficient and robust to achieve the hand detection process. The finger labeling makes the system achieve pose classification according to the finger name and the relationship between each fingers. It also make the classification more effective and accutate. Two kinds of gesture sets can be recognized by our system. According to the experiment, the average accuracy of American Sign Language(ASL) number gesture set is 94.33%, and that of general gestures set is 95.01%. Since our system runs in real-time and has a high recognition rate, we can embed it into various applications.

• Journal of IKEEE
• /
• v.22 no.2
• /
• pp.250-257
• /
• 2018

In this paper, a smart bus system that automates public bus traffic payment by applying beacon and computer vision and provides bus route information, real-time location information, getting off alarm is proposed. By using the beacon to recognize busses near the stop and to board the bus to be boarded, this system automatically processes the payment when boarding by using the distance from the beacon and the information provided by the beacon and the face comparison. After the payment processing, the system provides the route information of the boarded bus and the real-time bus location information to the user, and when the user sets an alarm using these informations, the alarm is activated when the bus leaves the bus stop.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.11 no.5
• /
• pp.313-323
• /
• 2016

With the development of autonomous vehicle, there has been active research on advanced driver assistance system for road marking detection using vision sensor and 3D Laser scanner. However, vision sensor has the weak points that detection is difficult in situations involving severe illumination variance, such as at night, inside a tunnel or in a shaded area; and that processing time is long because of a large amount of data from both vision sensor and 3D Laser scanner. Accordingly, this paper proposes a road marking detection and classification method using single 2D Laser scanner. This method road marking detection and classification based on accumulation distance data and intensity data acquired through 2D Laser scanner. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 3D Laser scanner-based method, thus demonstrating the possibility of road marking type classification using single 2D Laser scanner.