• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.5
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• pp.1220-1228
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• 1995

In this paper, we consider the problems of tracking an object in a real image. In evaluating these problems, we explore a new technique based on an active contour model commonly called a snake model, and propose the active bar models to represent target. Using this model, we simplified the target welection problems, reduced the search space of energy surface, and obtained the better performances than those of snake model. This approach improves the numerical stability and the tendency for points to bunch up and speed up the computational efficiency. Representing the object by active bar, we can easily obtain the zeroth, the first, and the second moment and it facilitates the target tracking. Finally, we present the good result for the visual tracking problem.

• 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.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.37-46
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• 2010

In this paper we have presented a two dimensional model based tracking system using improved chamfer matching. Conventional chamfer matching could not calculate similarity well between the object and image when there is very cluttered background. Then we have improved chamfer matching to calculate similarity well even in very cluttered background with edge and corner feature points. Improved chamfer matching is used as likelihood function of particle filter which tracks the geometric object. Geometric model which uses edge and corner feature points, is a discriminant descriptor in color changes. Particle Filter is more non-linear tracking system than Kalman Filter. Then the presented method uses geometric model, particle filter and improved chamfer matching for tracking object in complex environment. In experimental result, the robustness of our system is proved by comparing other methods.

• Journal of Broadcast Engineering
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• v.16 no.1
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• pp.178-181
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• 2011

With the development of mobile devices and intelligent surveillance system loaded with pan/tilt cameras, object tracking with non-stationary cameras has become a topic with increasing importancy. Since it is difficult to model a background image in a non-stationary camera environment, colors and texture are the most important features in the tracking algorithm. However, colors in the background similar to those in the target arise instability in the tracking. Recently, we proposed a robust color based tracking algorithm that uses an area weighted centroid shift. In this letter, we update the model such that it becomes more stable against background colors. The proposed algorithm also incorporates time filtering by adding an additional energy term to the energy functional.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.153-163
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• 2004

This paper presents a video tracking method for a deformable moving object using an active contour model in the image sequences. It is quite important to decide the local convergence directions of the contour points for correctly extracting the boundary of the moving object with deformable shape. For this purpose, an energy function for the active contour model is newly proposed by adding a directional energy term using a frame difference map to tile Greedy algorithm. In addition, an updating rule of tile frame difference map is developed to encourage the stable convergence of the contour points. Experimental results on a set of synthetic and real image sequences showed that the proposed method can fully track the deformable object while extracting the boundary of the object elaborately in every frame.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.5
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• pp.64-72
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• 2007

In this paper we proposed a combined active contour model and motion estimation-based object tracking technique. After assigning the initial contour, we find the object's boundary and update the initial contour by using object's motion information. In the following frames, similar snake algorithm is repeated to make continuously estimated object's region. The snake algerian plays a role in separating the object from background, while motion estimation provides object's moving direction and displacement. The proposed algorithm provides equivalently stable, robust, tracking performance with significantly reduced amount of computation, compared with the existing shape model-based algorithms.

• Journal of Advanced Navigation Technology
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• v.18 no.4
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• pp.387-392
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• 2014

Recently, various research for intelligent video surveillance system have been proposed, but the existing monitoring systems are inefficient because all of situational awareness is judged by the human. In this paper, shadow removal based moving object tracking method is proposed using the chromaticity and entropy image. The background subtraction model, effective in the context awareness environment, has been applied for moving object detection. After detecting the region of moving object, the shadow candidate region has been estimated and removed by RGB based chromaticity and minimum cross entropy images. For the validity of the proposed method, the highway video is used to experiment. Some experiments are conducted so as to verify the proposed method, and as a result, shadow removal and moving object tracking are well performed.

• Journal of Multimedia Information System
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• v.7 no.1
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• pp.11-16
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• 2020

Deep Learning has become the most important technology in the field of artificial intelligence machine learning, with its high performance overwhelming existing methods in various applications. In this paper, an interactive window service based on object recognition technology is proposed. The main goal is to implement an object recognition technology using this deep learning technology to remove the existing eye tracking technology, which requires users to wear eye tracking devices themselves, and to implement an eye tracking technology that uses only usual cameras to track users' eye. We design an interactive system based on efficient eye detection and pupil tracking method that can verify the user's eye movement. To estimate the view-direction of user's eye, we initialize to make the reference (origin) coordinate. Then the view direction is estimated from the extracted eye pupils from the origin coordinate. Also, we propose a blink detection technique based on the eye apply ratio (EAR). With the extracted view direction and eye action, we provide some augmented information of interest without the existing complex and expensive eye-tracking systems with various service topics and situations. For verification, the user guiding service is implemented as a proto-type model with the school map to inform the location information of the desired location or building.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.743-744
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• 2016

Object tracking in video sequences is a challenging task and has various applications. Particle filtering has been proven very successful for non-Gaussian and non-linear estimation problems. In this study, we first try to develop a color-based particle filter. In this approach, the color distributions of video frames are integrated into particle filtering. Color distributions are applied because of their robustness and computational efficiency. The model of the particle filter is defined by the color information of the tracked object. The model is compared with the current hypotheses of the particle filter using the Bhattacharyya coefficient. The proposed tracking method directly incorporates the scale and motion changes of the objects. Experimental results have been presented to show the effectiveness of our proposed system.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.712-715
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• 1997

In image jacobian based visual servoing, generally, inverse jacobian should be calculated by complicated coordinate transformations. These are required excessive computation and the singularity of the image jacobian should be considered. This paper presents a visual servoing to control the pose of the robotic manipulator for tracking and grasping 3-D moving object whose pose and motion parameters are unknown. Because the object is in motion tracking and grasping must be done on-line and the controller must have continuous learning ability. In order to estimate parameters of a moving object we use the kalman filter. And for tracking and grasping a moving object we use a fuzzy inference based reinforcement learning algorithm of dynamic recurrent neural networks. Computer simulation results are presented to demonstrate the performance of this visual servoing