• Journal of the Semiconductor & Display Technology
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• v.14 no.3
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• pp.67-72
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• 2015

This paper proposes an object tracking system based on keypoints using homography in mobile environments. The proposed system is based on markerless tracking, and there are four modules which are recognition, tracking, detecting and learning module. Recognition module detects and identifies an object to be matched on current frame correspond to the database using LSH through SURF, and then this module generates a standard object information. Tracking module tracks an object using homography information that generate by being matched on the learned object keypoints to the current object keypoints. Then update the window included the object for defining object's pose. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. The experimental results show that the proposed system is able to recognize and track objects with updating object's pose for the use of mobile platform.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.1015-1017
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• 2013

Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, non-rigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location or the shape of the object in every frame. This paper describes an object tracking system based on active vision with two cameras, into algorithm of single camera tracking system an object active visual tracking and object locked system based on Extend Kalman Filter (EKF) is introduced, by analyzing data from which the next running state of the object can be figured out and after the tracking is performed at each of the cameras, the individual tracks are to be fused (combined) to obtain the final system object track.

• The Journal of Korea Robotics Society
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• v.2 no.2
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• pp.129-136
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• 2007

This paper introduces a position-based robust visual servoing method which is developed for operation of a human-like robot with two arms. The proposed visual servoing method utilizes SIFT algorithm for object detection and CAMSHIFT algorithm for object tracking. While the conventional CAMSHIFT has been used mainly for object tracking in a 2D image plane, we extend its usage for object tracking in 3D space, by combining the results of CAMSHIFT for two image plane of a stereo camera. This approach shows a robust and dependable result. Once the robot's task is defined based on the extracted 3D information, the robot is commanded to carry out the task. We conduct several position-based visual servoing tasks and compare performances under different conditions. The results show that the proposed visual tracking algorithm is simple but very effective for position-based visual servoing.

• Journal of the Semiconductor & Display Technology
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• v.14 no.3
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• pp.73-77
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• 2015

This paper proposes an object tracking system according to reliability assessment of learning in mobile environments. The proposed system is based on markerless tracking, and there are four modules which are recognition, tracking, detecting and learning module. Recognition module detects and identifies an object to be matched on current frame correspond to the database using LSH through SURF, and then this module generates a standard object information that has the best reliability of learning. The standard object information is used for evaluating and learning the object that is successful tracking in tracking module. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. The experimental results show that the proposed system is able to recognize and track the reliable objects with reliability assessment of learning for the use of mobile platform.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.673-679
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• 2012

This paper introduces robust tracking algorithm for fast and erratic moving object. CAMSHIFT algorithm has less computation and efficient performance for object tracking. However, the method fails to track a object if it moves out of search window by fast velocity and/or large movement. The size of the search window in CAMSHIFT algorithm should be selected manually also. To solve these problems, we propose an efficient prediction technique for fast movement of object using Kalman Filter with automatic initial setting and variable configuration technique for search window. The proposed method is compared to the traditional CAMSHIFT algorithm for searching and tracking performance of objects on test image frames.

• Journal of the Semiconductor & Display Technology
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• v.15 no.3
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• pp.46-50
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• 2016

Recently, Depending on expectancy effect and ripple effect of augmented reality, the convergence between augmented reality and culture & arts are being actively conducted. This paper proposes a learning method for effective object tracking in 3D storytelling augmented reality in cultural properties. The proposed system is based on marker-less tracking, and there are four modules that are recognition, tracking, detecting and learning module. Recognition module is composed of SURF and LSH, and then this module generates standard object information. Tracking module tracks an object using object tracking based on reliability. This information is stored in Learning module along with learned time information. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. Also, it proposes a method for robustly implementing a 3D storytelling augmented reality in cultural properties in the future.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.4
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• pp.85-94
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• 2004

Object tracking in a real time image is one of interesting subjects in computer vision and many Practical application fields during the past couple of years. But sometimes existing systems cannot find all objects by recognizing background noise as object. This paper proposes a method of object detection and tracking using adaptive background image in real time. To detect object which is not influenced by illumination and to remove noise in background image, this system generates adaptive background image by real time background image updating. This system detects object using the difference between background image and input image from camera. After setting up Minimum Bounding Rectangle(MBR) using the internal point of detected object, the system tracks object through this MBR In addition, this paper evaluates the test result about performance of proposed method as compared with existing tracking algorithm.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.337-343
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• 2014

This paper proposes a minimal contour tracking algorithm that reduces transmission of data for tracking mobile objects in surveillance networks in terms of detection and communication load. This algorithm perform detection for object tracking and when it transmit image data to server from camera, it minimized communication load by reducing quantity of transmission data. This algorithm use minimal tracking area based on the kinematics of the object. The modeling of object's kinematics allows for pruning out part of the tracking area that cannot be mechanically visited by the mobile object within scheduled time. In applications to detect an object in real time,when transmitting a large amount of image data it is possible to reduce the transmission load.

• Journal of Broadcast Engineering
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• v.7 no.3
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• pp.248-261
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• 2002

In this paper, we proposed a new 3-dimensional(3-D) object-tracking algorithm that can control a stereo camera using a variable window mask supported by which uses ,B-D information and an optical BPEJTC. Hence, three-dimensional information characteristics of a stereo vision system, distance information from the stereo camera to the tracking object. can be easily acquired through the elements of a stereo vision system. and with this information, we can extract an area of the tracking object by varying window masks. This extractive area of the tracking object is used as the next updated reference image. furthermore, by carrying out an optical BPEJTC between a reference image and a stereo input image the coordinates of the tracking objects location can be acquired, and with this value a 3-D object tracking can be accomplished through manipulation of the convergence angie and a pan/tilt of a stereo camera. From the experimental results, the proposed algorithm was found to be able to the execute 3-D object tracking by extracting the area of the target object from an input image that is independent of the background noise in the stereo input image. Moreover a possible implementation of a 3-D tele-working or an adaptive 3-D object tracker, using the proposed algorithm is suggested.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.145-148
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• 1996

In moving object tracking based on the visual sensory feedback, a prerequisite is to determine which feature or which object is to be tracked and then the feature or the object identification precedes the tracking. In this paper, we focus on the object identification not image feature identification. The target identification is realized by finding out corresponding line segments to the hypothesized model segments of the target. The key idea is the combination of the Mahalanobis distance with the geometrica relationship between model segments and extracted line segments. We demonstrate the robustness and feasibility of the proposed target identification algorithm by a moving vehicle identification and tracking in the video traffic surveillance system over images of a road scene.