• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.95-101
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• 1999

In this paper, we propose an adaptive method of tracking multiple moving objects using contour and features in surrounding conditions. We use an adaptive background model for robust processing in surrounding conditions. Object segmentation model detects pixels thresholded from local difference image between background and current image and extracts connected regions. Data association problem is solved by using feature extraction and object recognition model in searching window. We use Kalman filters for real-time tracking. The results of simulation show that the proposed method is good for tracking multiple moving objects in highway image sequences.

• Spatial Information Research
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• v.14 no.2 s.37
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• pp.235-244
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• 2006

The MMS(Mobile Mapping System) using the vehicle equipped GPS, IMU and CCD Cameras is the effective system for the management of the road facilities, update of the digital map, and etc. The image, vehicle's 3 dimensional position and attitude information provided MMS is a important source for positioning objects included the image. In this research we applied the tracking technique to the specific object in image. The extraction of important object from immense MMS data makes more effectiveness in this system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4C
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• pp.401-410
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• 2007

In this paper, we propose water quality monitoring system using vision camera and multiple objects tracking method. The proposed system analyzes object individually using vision camera unlike monitoring system using sensor method. The system using vision camera consists of individual object segmentation part and objects tracking part based on interrelation between successive frames. For real-time processing, we make background image using non-parametric estimation and extract objects using background image. If we use non-parametric estimation, objects extraction method can reduce large amount of computation complexity, as well as extract objects more effectively. Multiple objects tracking method predicts next motion using moving direction, velocity and acceleration of individual object then carries out tracking based on the predicted motion. And we apply exception handling algorithms to improve tracking performance. From experiment results under various conditions, it shows that the proposed system can be available for real-time water quality monitoring system since it has very short processing time and correct multiple objects tracking.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.740-745
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• 2003

Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks suck as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool for rigid or non-rigid objects. Snake is designed no the basis of snake energies. Segmenting and tracking can be executed successfully by energy minimization. In this research, two new paradigms for segmentation and tracking are suggested. First, because the conventional method uses only intensity information, it is difficult to separate an object from its complex background. Therefore, a new energy and design schemes should be proposed for the better segmentation of objects. Second, conventional snake can be applied in situations where the change between images is small. If a fast moving object exists in successive images, conventional snake will not operate well because the moving object may have large differences in its position or shape, between successive images. Snakes's nodes may also fall into the local minima in their motion to the new positions of the target object in the succeeding image. For robust tracking, the condensation algorithm was adopted to control the parameters of the proposed snake model called "adaptive color snake model(SCSM)". The effectiveness of the ACSM is verified by appropriate simulations and experiments.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.2 no.2 s.3
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• pp.85-95
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• 2003

This paper proposes a multiple moving objects tracking system which is adaptable itself to circumstances. Snake model is sensitive to the start position value because it does not accurately express contours of objects in complex image. It can be improved as the proposed system gets background images by using difference images, segments objects using neighborhood pixels and assesses the position feature values acquired on the start position value to deformable Snake model. And also the system can simplify complex background images and reduce search regions by the constituent points of a Snake laid in Positions of object. It is showed that the proposed system can be appBied to multiple moving vehicle racking systems by the experimental results of 30fps AVI file.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.798-802
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• 2003

The robustness and reliability of vision algorithms is the key issue in robotic research and industrial applications. In this paper, the robust real time visual tracking in complex scene is considered. A common approach to increase robustness of a tracking system is to use different models (CAD model etc.) known a priori. Also fusion of multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. Because voting is a very simple or no model is needed for fusion, voting-based fusion of cues is applied. The approach for this algorithm is tested in a 3D Cartesian robot which tracks a toy vehicle moving along 3D rail, and the Kalman filter is used to estimate the motion parameters, namely the system state vector of moving object with unknown dynamics. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.131-134
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• 1995

We developed a visual system that is able to track the moving objects within a certain range of errors. The visual system is driven by two DC servo motors that are controlled by a computer based on the visual data obtained from a CCD video camera. The software to track the moving objects is developed based on the PWM of the DC motors. Also, the problems how to implement a fuzzy logic control method and a neural network in this system, are also considered in order to check the control performance of tracking. The fuzzy logic algorithm is a powerful control technique for nonlinear dynamical system and also the neural network could be implemented in this system. In this paper, we present configuration of tracking system developed in our laboratory, the control methods of the visual system and the experimental results are shown.

• Journal of Korea Multimedia Society
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• v.19 no.4
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• pp.774-779
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• 2016

The conventional methods using color signal for tracking the movement of the object require a lot of calculation and the performance is not accurate. In this paper, we propose a method to effectively track the moving objects using the depth information from a depth camera. First, it separates the background and the objects based on the depth difference in the depth of the screen. When an object is moved, the depth value of the object becomes blurred because of the phenomenon of Motion Blur. In order to solve the Motion Blur, we observe the changes in the characteristics of the object (the area of the object, the border length, the roundness, the actual size) by its velocity. The proposed algorithm was implemented in the simulation that was applied directly to the tracking of a golf ball. We can see that the estimated value of the proposed method is accurate enough to be very close to the actual measurement.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.295-296
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• 2006

This paper implements a method that tracking the moving objects that detected by the motion detection function of the digital CCTV system. We simply implement the motion detection function of the digital CCTV system that use frame difference and thresholding. When motion is detected, the motion detection function generates two outputs. One output is the event that the motion is arised in input image frame. The other output is coordinate that motion is exists. Then, do the block matching algorithm[2] using coordinate, that motion is exists, as initial coordinate of the block matching algorithm. The best matched coordinate is new initial coordinate of the block matching algorithm for the next image frame. We simply use the block matching algorithm that implements tracking the moving objects. It is simple, but useful the actual digital CCTV system.

• Journal of information and communication convergence engineering
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• v.8 no.4
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• pp.466-471
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• 2010

We have proposed a method to detect and track moving ships using position from Radar and image processor. Real-time segmentation of moving regions in image sequences is a fundamental step in the radar-camera integrated system. Algorithms for segmentation of objects are implemented by composing of background subtraction, morphologic operation, connected components labeling, region growing, and minimum enclosing rectangle. Once the moving objects are detected, tracking is only performed upon pixels labeled as foreground with reduced additional computational burdens.