• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.45 no.3
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• pp.177-187
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• 2009

This paper describes a automatic video surveillance system(AVSS) with long range and 360$^{\circ}$ coverage that is automatically rotated in an elevation over azimuth mode in response to the TTM(tracked target message) signal of vessels tracked by ARPA(automatic radar plotting aids) radar. This AVSS that is a video security and tracking system supported by ARPA radar, CCTV(closed-circuit television) camera system and other sensors to automatically identify and track, detect the potential dangerous situations such as collision accidents at sea and berthing/deberthing accidents in harbor, can be used in monitoring the illegal fishing vessels in inshore and offshore fishing ground, and in more improving the security and safety of domestic fishing vessels in EEZ(exclusive economic zone) area. The movement of the target vessel chosen by the ARPA radar operator in the AVSS can be automatically tracked by a CCTV camera system interfaced to the ECDIS(electronic chart display and information system) with the special functions such as graphic presentation of CCTV image, camera position, camera azimuth and angle of view on the ENC, automatic and manual controls of pan and tilt angles for CCTV system, and the capability that can replay and record continuously all information of a selected target. The test results showed that the AVSS developed experimentally in this study can be used as an extra navigation aid for the operator on the bridge under the confusing traffic situations, to improve the detection efficiency of small targets in sea clutter, to enhance greatly an operator s ability to identify visually vessels tracked by ARPA radar and to provide a recorded history for reference or evidentiary purposes in EEZ area.

• The Journal of the Korea Contents Association
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• v.2 no.2
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• pp.17-26
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• 2002

In this paper, we suggest a method to acquire the moving object centered video by panning and tilting a camera automatically according to motion vectors calculated by detecting the motion of a moving object on video steam. We create a difference image by estimating the intensity difference at the grid points of neighboring frames. And we detect the motion using both horizontal projection histogram and vertical projection histogram and decide the center of motion part. Then we calculate a new direction and degree of the motion by comparing coordinates at the center of current motion and the center of previous motion. By controling the RCM using these Motion vectors, we can get video stream positioned unwire object on the center of video frame. Through the experiments, we could get a moving object centered video stream continuously arid monitor remotely by implementing sever/client architecture based on the web.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.80-87
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• 2011

In this paper we present subject region-based auto-focusing algorithm using noise robust focus measure. The proposed algorithm automatically estimates the main subject using entropy and solves the traditional problems with a subject position or high frequency component of background image. We also propose a new focus measure by analyzing the discrete cosine transform coefficients. Experimental results show that the proposed method is more robust to Gaussian and impulse noises than the traditional methods. The proposed algorithm can be applied to Pan-tilt-zoom (PTZ) cameras in the intelligent video surveillance system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1823-1830
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• 2003

This paper presents a multiple face detector based on a robust pupil detection technique. The pupil detector uses active illumination that exploits the retro­reflectivity property of eyes to facilitate detection. The detection range of this method is appropriate for interactive desktop and kiosk applications. Once the location of the pupil candidates are computed, the candidates are filtered and grouped into pairs that correspond to faces using heuristic rules. To demonstrate the robustness of the face detection technique, a dual mode face tracker was developed, which is initialized with the most salient detected face. Recursive estimators are used to guarantee the stability of the process and combine the measurements from the multi­face detector and a feature correlation tracker. The estimated position of the face is used to control a pan­tilt servo mechanism in real­time, that moves the camera to keep the tracked face always centered in the image.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.939-946
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• 2018

In this paper, we implement a viewer program that can monitor effectively using omni-directional images. The program consists of four modes: Normal mode, ROI(Region of Interest) mode, Tracking mode, and Auto-rotation mode, and the results for each mode is displayed simultaneously. In the normal mode, the wide angle image is rendered as a spherical image to enable pan, tilt, and zoom. In ROI mode, the area is displayed expanded by selecting an area. And, in Auto-rotation mode, it is possible to track the object by mapping the position of the object with the rotation angle of the spherical image to prevent the object from deviating from the spherical image in Tracking mode. Parallel programming for processing of multiple modes is performed to improve the processing speed. This has the advantage that various angles can be seen compared with surveillance system having a limited angle of view.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.1-8
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• 2005

In this paper, we propose a two-stage head tracking algorithm adequate for real-time active camera system having pan-tilt-zoom functions. In the color convergence stage, we first assume that the shape of a head is an ellipse and its model color histogram is acquired in advance. Then, the min-shift method is applied to roughly estimate a target position by examining the histogram similarity of the model and a candidate ellipse. To reflect the temporal change of object color and enhance the reliability of mean-shift based tracking, the target histogram obtained in the previous frame is considered to update the model histogram. In the updating process, to alleviate error-accumulation due to outliers in the target ellipse of the previous frame, the target histogram in the previous frame is obtained within an ellipse adaptively shrunken on the basis of the model histogram. In addition, to enhance tracking reliability further, we set the initial position closer to the true position by compensating the global motion, which is rapidly estimated on the basis of two 1-D projection datasets. In the subsequent stage, we refine the position and size of the ellipse obtained in the first stage by using shape information. Here, we define a robust shape-similarity function based on the gradient direction. Extensive experimental results proved that the proposed algorithm performs head hacking well, even when a person moves fast, the head size changes drastically, or the background has many clusters and distracting colors. Also, the propose algorithm can perform tracking with the processing speed of about 30 fps on a standard PC.