• Journal of Korea Multimedia Society
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• v.16 no.11
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• pp.1261-1271
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• 2013

Zoom tracking involves the automatic adjustment of the focus motor in response to the zoom motor movements for the purpose of keeping an object of interest in focus, and is typically achieved by moving the zoom and focus motors in a zoom lens module so as to follow the so-called "trace curve", which shows the in-focus motor positions versus the zoom motor positions for a specific object distance. Thus, one can simply implement zoom tracking by following the most closest trace curve after all the trace curve data are stored in memory. However, this approach is often prohibitive in practical implementation because of its large memory requirement. Many other zoom tracking methods such as GZT, AZT and etc. have been proposed to avoid large memory requirement but with a deteriorated performance. In this paper, we propose a new zoom tracking method called 'Approximate Feedback Zoom Tracking method (AFZT)' on DM36x-based IP network camera, which does not need large memory by approximating nearby trace curves, but generates better zoom tracking accuracy than GZT or AZT by utilizing focus value as feedback information. Experiments through real implementation shows the proposed zoom tracking method improves the tracking performance and works in real-time.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.343-346
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• 2002

This paper presents an advanced auto focus camera system using the adaptive zoom tracking method. The proposed system can achieve an accurate zoom tracking with significantly reduced system memory.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.3
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• pp.55-63
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• 2008

This paper presents a panorama background generation and object tracking technique using a Pan-Tilt-Zoom camera. The proposed method estimates local motion vectors rapidly using phase correlation matching at the prespecified multiple local regions, and it makes minimized estimation error by vector quantization. We obtain the required image patches, by estimating the overlapped region using local motion vectors, we can then project the images to cylinder and realign the images to make the panoramic image. The object tracking is performed by extracting object's motion and by separating foreground from input image using background subtraction. The proposed PTZ-based object tracking method can efficiently generated a stable panorama background, which covers up to 360 degree FOV The proposed algorithm is designed for real-time implementation and it can be applied to many commercial applications such as object shape detection and face recognition in various surveillance video systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3C
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• pp.271-277
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• 2010

In this paper we proposed an object tracking system using PTZ camera. Once the target object is detected, the CAMshift tracking algorithm focuses it in realtime mode as the camera is moving accordingly. Since the CAMShift algorithm takes into account the object size, zoom related tracking is possible. We used the spherical coordinate to gain pan and tilt position. The position information is used to set the center of target object in the middle of the image by using the PTZ protocol and RS-485 interface. Our system showed excellent experimental results in various environments.

• Korean Journal of Cognitive Science
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• v.16 no.4
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• pp.333-349
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• 2005

In this paper, we propose a real tine moving object tracking system based on block-based image processing technique and human visual processing. The system has two nun features. First, to take advantage of the merit of the biological mechanism of human retina, the system has two cameras, a CCD(Charge-Coupled Device) camera equipped with wide angle lens for more wide scope vision and a Pan-Tilt-Zoon tamers. Second, the system divides the input image into a numbers of blocks and processes coarsely to reduce the rate of tracking error and the processing time. Tn an experiment, the system showed satisfactory performances coping with almost every noisy image, detecting moving objects very int and controlling the Pan-Tilt-Zoom camera precisely.

• KIISE Transactions on Computing Practices
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• v.21 no.9
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• pp.610-615
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• 2015

The accuracy and precision of video-based remote gaze trackers is affected by numerous factors (e.g. the head movement of the participant). However, it is challenging to control all factors that have an influence, and doing so (e.g., using a chin-rest to control geometry) could lead to losing the benefit of using gaze trackers, i.e., the ecological validity of their unobtrusive nature. We propose an adaptive zoom-based gaze tracking technique, ZoomTrack that addresses this problem by improving the resolution of the gaze tracking results. Our approach magnifies a region-of-interest (ROI) and retrieves gaze points at a higher resolution under two different zooming modes: only when the gaze reaches the ROI (temporary) or whenever a participant stares at the stimuli (omnipresent). We compared these against the base case without magnification in a user study. The results are then used to summarize the advantages and limitations of our technique.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.395-401
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• 2021

In the digital era, Korean universities are using real-time remote lecture platforms as a tool for learning. However, it is confusing due to low usage experience problems, and usability studies are needed. The purpose of this study is to measure, analyze, and propose gaze tracking and learning experiences according to the UI design layout of ZOOM. A total of 30 subjects were selected to conduct eye tracking and survey when learning. The experiment showed that grid layouts have a higher emotional realism and learning satisfaction than other layouts. Furthermore, it turns out that participants focused the most attention on grid layouts. Therefore, for high learning experience, grid layouts and layouts that visually highlight participants and induce communication are needed.

• Journal of the Korea Society of Computer and Information
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• v.16 no.6
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• pp.89-97
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• 2011

When examining current process of object tracking and search, objects were tracked by extracting them from image that was inputted through fixed single camera and objects were recognized through Zoom function to know detailed information on objects tracked. This study proposed system that expresses information on area that can seek and recognize object tracked as augmented reality by recognizing and seeking object by using multi camera. The result of experiment on proposed system showed that the number of pixels that was included in calculation was remarkably reduced and recognition rate of object was enhanced and time that took to identify information was shortened. Compared with existing methods, this system has advantage of better accuracy that can detect the motion of object and advantage of shortening time that took to detect motion.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.931-933
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• 2005

감시 시스템에서 많이 사용되는 팬틸트줌(Pan-Tilt-Zoom) 카메라로 객체 검출과 추적을 할 때 카메라를 섬세하게 제어하는 것이 중요하다. 본 논문은 팬틸트줌 카메라를 이용하여 얼굴을 검출 및 추적하는 감시 시스템 구성과 카메라 제어 방법을 제안한다. 얼굴 검출을 위해서 P. Viola가 제안한 Haar-like feature를 이용한 빠른 객체 검출방법을 이용하고 얼굴 추적을 위해서 CAMSHIFT와 AAM을 이용하여 얼굴 추적과 얼굴 특징 정보 추출이 가능한 감시 시스템 구현을 하였다.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.802-812
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• 2019

With the rapid development in the field of unmanned aerial vehicles(UAVs) and drones, higher request to development of a surveillance system for a drone is putting forward. Since surveillance systems with fixed cameras have a limited range, a development of surveillance systems with a moving camera applicable to PTZ(Pan-Tilt-Zoom) cameras is required. Selecting the features for object plays a critical role in tracking, and the object has to be represented by their shapes or appearances. Considering these conditions, in this paper, an object tracking method with optical flow is introduced to track a tiny drone with a moving camera. In addition, a tracking method combined with kalman filter is proposed to track continuously even when tracking is failed. Experiments are tested on sequences which have a target from the minimal 12 pixels to the maximal 56337 pixels, the proposed method achieves average precision of 175% improvement. Also, experimental results show the proposed method tracks a target which has a size of 12pixels.