• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.1003-1004
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• 2006

In this paper we extract the position value of the tracking object using the hierarchical optic-digital algorithm and to control the main visual angle and Pan/Tilt. And then we propose the optic-digital stereo object tracking system for adaptive extracting the moving-target.

• Journal of the HCI Society of Korea
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• v.6 no.1
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• pp.9-16
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• 2011

The previous researches of 3D object modeling have been performed in a limited environment where a target object only exists. However, in order to model an object in the real environment, we need to consider a dynamic environment, which has various objects and a frequently changing background. Therefore, this paper presents a segmentation and tracking method for a foreground which includes a target object in the dynamic environment. By using depth information than color information, the foreground region can be segmented and tracked more robustly. In addition, the foreground region can be tracked on the sequential images by referring depth distributions of the foreground region because both the position and the status in the consecutive images of the foreground region are almost unchanged. Experimental results show that our proposed method can robustly segment and track the foreground region in various conditions of the real environment. Moreover, as an application of the proposed method, it is presented a method for modeling an object extracting the object regions from the foreground region that is segmented and tracked.

• The Journal of Korea Robotics Society
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• v.15 no.1
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• pp.8-15
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• 2020

This research is a case study of underwater object tracking based on real-time recurrent regression networks (Re³). Re³ has the concept of generic object tracking. Because of these characteristics, it is very effective to apply this model to unclear underwater sonar images. The model also an pursues object tracking method, thus it solves the problem of calculating load that may be limited when object detection models are used, unlike the tracking models. The model is also highly intuitive, so it has excellent continuity of tracking even if the object being tracked temporarily becomes partially occluded or faded. There are 4 types of the dataset using multi-beam sonar images: including (a) dummy object floated at the testbed; (b) dummy object settled at the bottom of the sea; (c) tire object settled at the bottom of the testbed; (d) multi-objects settled at the bottom of the testbed. For this study, the experiments were conducted to obtain underwater sonar images from the sea and underwater testbed, and the validity of using noisy underwater sonar images was tested to be able to track objects robustly.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.8
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• pp.746-751
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• 2006

In this paper, we present tracking algorithm for the indoor moving object. We research passive method using a camera and image processing. It had been researched to use dynamic based estimators, such as Kalman Filter, Extended Kalman Filter and Particle Filter for tracking moving object. These algorithm have a good performance on real-time tracking, but they have a limit. If the shape of object is changed or object is located on complex background, they will fail to track them. This problem will need the complicated image processing algorithm. Finally, a large algorithm is made from integration of dynamic based estimator and image processing algorithm. For eliminating this inefficiency problem, image based estimator, Mean-shift Algorithm is suggested. This algorithm is implemented by color histogram. In other words, it decide coordinate of object's center from using probability density of histogram in image. Although shape is changed, this is not disturbed by complex background and can track object. This paper shows the results in real camera system, and decides 3D coordinate using the data from mean-shift algorithm and relationship of real frame and camera frame.

• 한국정보디스플레이학회:학술대회논문집
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• 2000.01a
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• pp.79-80
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• 2000

General stereo vision system shows things in 3D, using two visions of left and right side. When the viewpoints of left/right sides are not in accord with each other, it gives fatigue to human eyes and prevents them from having the 3-D feeling. Also, it would be difficult to track mobile objects that are not in the middle of a screen. Therefore, the object tracking function of stereo vision system is to control tracking objects to always be in the middle of a screen while controlling convergence angles of mobile objects in the input image of the left/right cameras. In this paper, object-tracker in stereo vision system is presented which would track mobile objects by using block matching algorithm of preprocessing and JTC.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.537-542
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• 2011

A mirror neuron is a neuron fires both when an animal acts and when the animal observes the same action performed by another. We propose a method of 3D reconstruction for occluded object motion tracking like Mirror Neuron System to fire in hidden condition. For modeling system that intention recognition through fire effect like Mirror Neuron System, we calculate depth information using stereo image from a stereo camera and reconstruct three dimension data. Movement direction of object is estimated by optical flow with three-dimensional image data created by three dimension reconstruction. For three dimension reconstruction that enables tracing occluded part, first, picture data was get by stereo camera. Result of optical flow is made be robust to noise by the kalman filter estimation algorithm. Image data is saved as history from reconstructed three dimension image through motion tracking of object. When whole or some part of object is disappeared form stereo camera by other objects, it is restored to bring image date form history of saved past image and track motion of object.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.175-178
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• 2010

Slow movement of snail can be a benefit since it means less speed of tracking is required to get accurate movement track, but in the other side it is difficult to extract the object because the snail is almost as static as the background. In this paper, we present a technique to track the snail by using one of its common characteristic, dark color of its shell. The technique needs to be robust to illumination change since the experiment is usually to observe the movement of snail both at bright and dim condition. Snail position coordinate in 3D space is calculated using orthogonal stereo vision which combines the information from two images taken from cameras at the top and in front of the aquarium. Experimental results show this technique does not need prior background image extraction and robust to gradual or sudden illumination change.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.767-774
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• 2005

In this paper, we present a snake-based scheme for tracking object contour using disparity information taken from a stereo image sequence with cluttered background. The proposed method is composed of two steps. First, 3-D motion of object is estimated and candidate snake points are selected in disparity space. Second, object contour is extracted by using a modified snake algorithm with disparity information. The proposed algorithm can successfully extract the concave contour of objects and track the object contour in complex image. Performance of the proposed algorithm has been verified by simulation.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.130-138
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• 2024

High-precision 3D Object Detection is a crucial component within autonomous driving systems, with far-reaching implications for subsequent tasks like multi-object tracking and path planning. In this paper, we propose a novel approach designed to enhance the performance of 3D Object Detection, especially in heading angle estimation by employing a moving object segmentation technique. Our method starts with extracting point-wise moving labels via a process of moving object segmentation. Subsequently, these labels are integrated into the LiDAR Pointcloud data and integrated data is used as inputs for 3D Object Detection. We conducted an extensive evaluation of our approach using the KITTI-road dataset and achieved notably superior performance, particularly in terms of AOS, a pivotal metric for assessing the precision of 3D Object Detection. Our findings not only underscore the positive impact of our proposed method on the advancement of detection performance in lidar-based 3D Object Detection methods, but also suggest substantial potential in augmenting the overall perception task capabilities of autonomous driving systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.551-562
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• 2024

This study relates to a method and device for extracting and tracking moving objects. In particular, objects are extracted using different images between adjacent images, and the location information of the extracted object is continuously transmitted to provide accurate location information of at least one moving object. It relates to a method and device for extracting and tracking moving objects based on tracking moving objects. People tracking, which started as an expression of the interaction between people and computers, is used in many application fields such as robot learning, object counting, and surveillance systems. In particular, in the field of security systems, cameras are used to recognize and track people to automatically detect illegal activities. The importance of developing a surveillance system, that can detect, is increasing day by day.