• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.95.2-95
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• 2002

1. Introduction of Camera Detection system Camera Detection system is an equipment that can detect realtime traffic information by image processing techniques. This information can be used to analyze and control road traffic flow. It is also used as a method to detect and control traffic flow for ITS(Intelligent Transportation System). Traffic information includes speed, head way, traffic flow, occupation time and length of queue. There are many detection systems for traffic data. But video detection system can detect multiple lanes with only one camera and collect various traffic information. So it is thought to be the most efficient method of all detection system. Though the...

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.12
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• pp.1033-1041
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• 2003

A new visual tracking scheme is proposed for a mobile robot that tracks a moving object in 3D space in real time. Visual tracking is to control a mobile robot to keep a moving target at the center of input image at all time. We made it possible by simplifying the relationship between the 2D image frame captured by a single camera and the 3D workspace frame. To precisely calculate the input vector (orientation and distance) of the mobile robot, the speed vector of the target is determined by eliminating the speed component caused by the camera motion from the speed vector appeared in the input image. The problem of temporary disappearance of the target form the input image is solved by selecting the searching area based on the linear prediction of target motion. The experimental results have shown that the proposed scheme can make a mobile robot successfully follow a moving target in real time.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.798-803
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• 2007

In case of minimized digital camera and mobile digital camera, it's difficult to get the high quality image by conventional AE(Auto Exposure) algorithm because of restriction of system organization. In this paper, a new algorithm that adopts a target setting, a compensation of feedback delay and a gamma correction, etc, are suggested for improving a noise increase and an output sensitivity decrease due to system minimization. We also suggest a method using fuzzy logic which can decide more effectively the ES(Electric Shutter) value and the AGC(Analog Gain Control) value than conventional system.

• Journal of Industrial Technology
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• v.20 no.B
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• pp.195-200
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• 2000

This paper describes the path planning method in an unknown environment for an autonomous mobile robot equipped with CCD(Charge-Coupled Device) camera. The mobile robot moves along the guideline. The CCD camera is used for the detection of the existence of a guideline. The wavelet transform is used to find the edge of guideline. It is possible for us to do image processing more easily and rapidly by using wavelet transform. We make a fuzzy control rule using image data as an input then determined the position and the navigation of the mobile robot. The center value of guideline is the input of fuzzy logic controller and the steering angle of the mobile robot is the fuzzy controller output. Some actual experiments show that the mobile robot effectively moves to target position by means of the applied fuzzy control.

• Proceedings of the KIEE Conference
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• 2001.10a
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• pp.230-232
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• 2001

The camera system we researched has two stepping motors for the pan and tilt operation, and the human face tracking algorithm. Recently, this kind of the camera is used in PC communication, telecommunication vision meeting and tele-lecture. This paper discusses the smooth speed control method of this camera when the face is moved to up, down, left and right direction. We used a mean shift algorithm for the face-tracking, proposed the speed control algorithm using a fuzzy logic and certified this characteristics with the experiment.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.3
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• pp.212-224
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• 2010

The usefulness of the magnetic camera for non-destructive testing of aging aircraft is discussed in this paper. The magnetic camera can be used f magnetic particle testing(MT), magnetic flux leakage testing(MFLT), eddy current testing(ECT) and penetration testing(PT). It measures the distribution of a magnetic field and visualizes the magnetic pattern. Near and far side cracks, fatigue, thickness degradation, and cracks under rivets have been detected. The possibility of quantitative evaluation was also examined. Using indirect experiments, we verified the detection ability of the sensor for cracks in titanium and advanced composite materials.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.785-796
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• 2019

Due to the view point, illumination, personal gait and other background situation, person re-identification across cameras has been a challenging task in video surveillance area. In order to address the problem, a novel method called Joint Bayesian across different cameras for person re-identification (JBR) is proposed. Motivated by the superior measurement ability of Joint Bayesian, a set of Joint Bayesian matrices is obtained by learning with different camera pairs. With the global Joint Bayesian matrix, the proposed method combines the characteristics of multi-camera shooting and person re-identification. Then this method can improve the calculation precision of the similarity between two individuals by learning the transition between two cameras. For investigating the proposed method, it is implemented on two compare large-scale re-ID datasets, the Market-1501 and DukeMTMC-reID. The RANK-1 accuracy significantly increases about 3% and 4%, and the maximum a posterior (MAP) improves about 1% and 4%, respectively.

• Korean Journal of Remote Sensing
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• v.24 no.6
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• pp.641-644
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• 2008

In order to improve a camera rotation calculation based on the bundle adjustment in Chon's camera motion (Chon and Shankar, 2007, 2008), this paper introduces a method calculating the camera rotation. It estimates a unit vector in the optical axis of a camera through the angles between the optical axis and vectors passing a camera position and ground control points (GCP). The camera position is estimated by using the inner product method proposed by Chon. The horizontal and vertical unit vectors of the camera are determined by using Yakimovsky and Cunningham's camera model (CAHV) (1978).

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.8
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• pp.2062-2072
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• 1994

This paper presents the development of a fuzzy controller for driving camera pan/tilt device so that the camera's viewing direction can automatically track a moving object. To achieve computational efficiency a non-contact type displacement follower is used as a feedback sensor instead of a vision camera. The displacement follower, however, is extremely sensitive to object's lighting condition and results in unstable response at high speed. To this end, a fuzzy controller is developed in such a way to provide stable tracking performance at high speed where the sensory signal is subjected to intermittant disturbances of large magnitude. The test result shows stable tracking response even for high speed and non-uniform lighting condition. The resulting camera autotracking system can be adopted as an effective tool for visual transfer in the context of teleoperation and autonomous robotics.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.390-395
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• 2009

In this paper, we design an on-road vehicle detection system based on the combination of a camera and a LIDAR system. In the proposed system, the candidate area is selected from the LIDAR data using a grouping algorithm. Then, the selected candidate area is scanned by an SVM to find an actual vehicle. The morphological edged images are used as features in a camera. The principal components of the edged images called eigencar are employed to train the SVM. We conducted experiments to show that the on-road vehicle detection system developed in this paper demonstrates about 80% accuracy and runs with 20 scans per second on LIDAR and 10 frames per second on camera.