• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.2
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• pp.338-347
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• 2005

In this paper, a decimation-in-time search direction algorithm for displacement prediction of moving object is proposed. The initialization of the proposed algorithm for moving direction prediction is performed by detecting moving objects at sequential frames and by obtaining a moving angle and a moving distance. A moving direction of the moving object at current frame is obtained by applying the decimation-in-time search direction mask. The decimation-in-tine search direction mask is that the moving object is detected by thinning out frames among the sequential frames, and the moving direction of the moving object is predicted by the search mask which is decided by obtaining the moving angle of the moving object in the 8 directions. to examine the propriety of the proposed algorithm, velocities of a driving car are measured and tracked, and to evaluate the efficiency, the proposed algorithm is compared to the full search algorithm. The evaluated results show that the number of displacement search times is reduced up to 91.8$\%$ on the average in the proposed algorithm, and the processing time of the tracking is 32.1ms on the average.

• Journal of Digital Convergence
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• v.10 no.2
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• pp.211-216
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• 2012

There are two kinks of processes could detect the laser pointer. One is the process which detects the location of the pointer. the other one is a possibility of dividing with the process which converts the coordinate of the laser pointer which is input in coordinate of the monitor. The previous Mean-Shift algorithm is not appropriately for real-time video image to calculate many quantity. In this paper, we proposed the context driven real-time laser pointer detection and tracking. The proposed method is a possibility of getting the result which is fixed from the situation which the background and the background which are complicated dynamically move. In the actual environment, we can get to give constant results when the object come in, when going out at forecast boundary. Ultimately, this paper suggests empirical application to verify the adequacy and the validity with the proposed method. Accordingly, the accuracy and the quality of image recognition will be improved the surveillance system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.2
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• pp.129-136
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• 2014

Target-tracking systems are important for carrying out effective surveillance missions using mobile surveillance robots. In this paper, we propose a target-tracking algorithm using camera image data for a three-axis mobile surveillance robot and carry out an actual hardware test for verifying the proposed algorithm. The heading direction vector of a camera system is deduced from the position error between the viewfinder center and the object center in a camera image. The position error is obtained using the CAMShift(Continuously Adaptive Mean Shift) algorithm, an image processing technique. The performance test of an actual three-axis mobile surveillance robot was carried out for verifying the proposed target-tracking algorithm in a real environment.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.237-250
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• 2022

In this study, we propose a method to estimate the biomass of invertebrate grazers from the videos with underwater drones by using a multi-object tracking model based on deep learning. In order to detect invertebrate grazers by classes, we used YOLOv5 (You Only Look Once version 5). For biomass estimation we used DeepSORT (Deep Simple Online and real-time tracking). The performance of each model was evaluated on a workstation with a GPU accelerator. YOLOv5 averaged 0.9 or more mean Average Precision (mAP), and we confirmed it shows about 59 fps at 4 k resolution when using YOLOv5s model and DeepSORT algorithm. Applying the proposed method in the field, there was a tendency to be overestimated by about 28%, but it was confirmed that the level of error was low compared to the biomass estimation using object detection model only. A follow-up study is needed to improve the accuracy for the cases where frame images go out of focus continuously or underwater drones turn rapidly. However,should these issues be improved, it can be utilized in the production of decision support data in the field of invertebrate grazers control and monitoring in the future.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.589-596
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• 2013

With the increase of social crime rate, the interest on the intelligent security system is also growing. This paper proposes a detection system of monitoring whether abnormal behavior is being carried in the images captured using CCTV. After detection of an object via subtraction from background image and morpholgy, this system extracts an abnormal behavior by each object's feature information and its trajectory. When an object is loitering for a while in CCTV images, this system considers the loitering as an abnormal behavior and sends the alarm signal to the control center to facilitate prevention in advance. Especially, this research aims at detecting a loitoring act among various abnormal behaviors and also extends to the detection whether an incoming object is identical to one of inactive objects out of image.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.1
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• pp.174-184
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• 1997

This paper presents a robust force tracking control of a smart flexible gripper featured by a piezoceramic actuator characterizing its durability and quick response time. A mathematical governing equation for the proposed gripper structure is derived by employing Hamilton's principle and a state space control model is subsequently obtained through model analysis. Uncertain system parameters such as frequency variation are included in the control model. A sliding mode control theory which has inherent robustness to the sys- tem uncertainties is adopted to design a force tracking controller for the piezoceramic actuator. Using out- put information from the tip force sensor, a full-order observer is constructed to estimate state variables of the system. Force tracking performances for desired trajectories represented by sinusoidal and step func- tions are evaluated by undertaking both simulation and experimental works. In addition, in order to illustrate practical feasibility of the proposed method, a two-fingered gripper is constructed and its performance is demonstrated by showing a capability of holding an object.

• Journal of KIISE:Databases
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• v.32 no.1
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• pp.71-85
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• 2005

Various researches on moving object modeling, uncertainty processing, and moving object indexing have been tarried out in the field of moving object databases. However. previous location tracking systems cannot efficiently retrieve location data of vehicles, because they manage all location information of vehicles using the conventional database. In this paper, we design the vehicle location management systen that is able to manage and retrieve vehicle locations efficiently in mobile environment. The proposed system consists of a server for managing vehicle locations and mobile clients. The system is able to not only process spatiotemporal queries related to locations of moving vehicles but also Provide moving vehicles' locations which are not stored in the system. The system is also able to manage vehicle location data effectively using a moving object index.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.1
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• pp.122-129
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• 1998

For generation and analysis of the multi-sensory image, we propose a new three dimensional (3D) modeling method considering an iternal heat source. We represent the heat conduction process within th object as an equivalent thermal circuit. Therefore, without a complex computation, our modeling approach can obtain thermal features of the object. By using the faceted model, the proposed method can express the accurate visual signatures of the object. Comparing the estimates datum with the obtained surface temperatures, we have demonstrated that the proposed method can provide a precise thermal features. The thermal images by out model is applicable to simulate a tracking loop of an IR missile.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1843-1844
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• 2008

In this paper, the distance from the camera to the object is measured by using stereo camera. The stereo camera acquires two images which have a disparity from left and right camera. The disparity could be calculated by stereo matching of two images and using the disparity, the distance was calculated by triangular method. The distance estimation was carried out as widen the distance from the camera to the object with several operation methods of stereo matching. The result of experiment was under 10 percent error compared with the real distance. Therefore, by using this result, stereo camera would be adapt to the object tracking robot research or any other related area.

• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.529-537
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• 2011

In this paper, we propose a stable scale adaptive tracking method that uses centroids of the target colors. Most scale adaptive tracking methods have utilized histograms to determine target window sizes. However, in certain cases, histograms fail to provide good estimates of target sizes, for example, in the case of occlusion or the appearance of colors in the background that are similar to the target colors. This is due to the fact that histograms are related to the numbers of pixels that correspond to the target colors. Therefore, we propose the use of centroids that correspond to the target colors in the scale adaptation algorithm, since centroids are less sensitive to changes in the number of pixels that correspond to the target colors. Due to the spatial information inherent in centroids, a direct relationship can be established between centroids and the scale of target regions. Generally, after the zooming factors that correspond to all the target colors are calculated, the unreliable zooming factors are filtered out to produce a reliable zooming factor that determines the new scale of the target. Combined with the centroid based tracking algorithm, the proposed scale adaptation method results in a stable scale adaptive tracking algorithm. It tracks objects in a stable way, even when the background colors are similar to the colors of the object.