• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.3
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• pp.181-187
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• 2003

For the moving objects with environmental sensors such as object tracking moving robot with audio and video sensors, environmental information acquired from sensors keep changing according to movements of objects. In such case, due to lack of adaptability and system complexity, conventional control schemes show limitations on control performance, and therefore, sensory-motor systems, which can intuitively respond to various types of environmental information, are desirable. And also, to improve the system robustness, it is desirable to fuse more than two types of sensory information simultaneously. In this paper, based on Braitenberg's model, we propose a sensory-motor based fusion system, which can trace the moving objects adaptively to environmental changes. With the nature of direct connecting structure, sensory-motor based fusion system can control each motor simultaneously, and the neural networks are used to fuse information from various types of sensors. And also, even if the system receives noisy information from one sensor, the system still robustly works with information from other sensors which compensates the noisy information through sensor fusion. In order to examine the performance, sensory-motor based fusion model is applied to object-tracking four-foot robot equipped with audio and video sensors. The experimental results show that the sensory-motor based fusion system can tract moving objects robustly with simpler control mechanism than model-based control approaches.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.743-748
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• 2011

In this paper, we propose a collaborative tracking algorithm for intelligent video surveillance systems using the multiple network cameras. To do this, each camera detects a moving object and it's movement direction by motion templates. Once a moving object is detect, the Kalman filter is used to reduce noises, and a collaborative tracking camera is selected according to the movement direction and the camera state. In this procedure, Pan-Tilt-Zoom(PTZ) parameters are assigned to obtain clear images. Finally, some experiments show the validity of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11C
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• pp.1551-1563
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• 2004

This paper proposes a hierarchical approach to shape model ASM using wavelet transform. Local structure model fitting in the ASM plays an important role in model-based pose and shape analysis. The proposed algorithm can robustly find good solutions in complex images by using wavelet decomposition. we also proposed effective method that estimates and corrects object's movement by using Wavelet transform-based hierarchical motion estimation scheme for ASM-based, real-time video tracking. The proposed algorithm has been tested for various sequences containing human motion to demonstrate the improved performance of the proposed object tracking.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.2
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• pp.65-70
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• 2008

Smart surveillance, is the use of automatic video analysis technologies in video surveillance applications. We present a robust object tracking method using pan-tilt-zoom camera for intelligent surveillance System, As the result of the experiment using 78 vehicle, the success rate of the tracking for moving object & non-moving object werw 97.4% and 91%. and 84.6%. the success rate o PTZ control for license plate image.

• Journal of Korea Game Society
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• v.20 no.6
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• pp.43-52
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• 2020

This paper presents a fast pattern tracking method using location prediction in cubemap video for 360-degree VR. A spherical cubemap frame has six face textures and searching a pattern is much slower than a flat image. To overcome the limitation, we propose a method of predicting the location of target pattern using Kalman filter and reducing the search area by considering only textures of predicted location. The experimental results showed that the proposed system is much faster than the previous method of searching all six faces and also gives accurate pattern tracking performance.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.3
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• pp.125-132
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• 2023

Roundabouts have strengths in traffic flow and safety but can present difficulties for inexperienced drivers. Demand to acquire and analyze drone images has increased to enhance a traffic environment allowing drivers to deal with roundabouts easily. In this paper, we propose a roundabout traffic analysis system that detects, tracks, and analyzes vehicles using a deep learning-based object detection model (YOLOv7) in drone images. About 3600 images for object detection model learning and testing were extracted and labeled from 1 hour of drone video. Through training diverse conditions and evaluating the performance of object detection models, we achieved an average precision (AP) of up to 97.2%. In addition, we utilized SORT (Simple Online and Realtime Tracking) and OC-SORT (Observation-Centric SORT), a real-time object tracking algorithm, which resulted in an average MOTA (Multiple Object Tracking Accuracy) of up to 89.2%. By implementing a method for measuring roundabout entry speed, we achieved an accuracy of 94.5%.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1153-1160
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• 2022

The application of video processing techniques are useful for the safety of nuclear power plants by tracking the people online on video to estimate the dose received by staff during work in nuclear plants. Nuclear reactors remotely visually controlled to evaluate the plant's condition using video processing techniques. Internal reactor components should be frequently inspected but in current scenario however involves human technicians, who review inspection videos and identify the costly, time-consuming and subjective cracks on metallic surfaces of underwater components. In case, if any frame of the inspection video degraded/corrupted/missed due to noise or any other factor, then it may cause serious safety issue. The problem of missing/degraded/corrupted video frame estimation is a challenging problem till date. In this paper a systematic literature review on video processing techniques is carried out, to perform their suitability analysis for NPP applications. The limitation of existing approaches are also identified along with a roadmap to overcome these limitations.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.4
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• pp.399-406
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• 2008

Tracking system is developed to receive both telemetry data and video data which are transmitted by radio frequency for the flight test of high manuvering vehicle. Tracking method of tracking system is the phase comparative monopulse that produce tracking data by measuring the phase difference of signal which is received by four patch antennas. The performance of tracking system is verified by flight test for RC helicopter and rocket.

• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.213-214
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• 2014

본 논문에서는 증강현실 저작도구를 사용 할 때 마커인식을 통하여 동영상의 Tracking 데이터를 추출하는 방법을 제안한다. 실험에 이용한 마커는 직사각형모양의 특징점이 잘 나타나는 물체로서, 사각형 마커인식을 위해 CornerDetection과 Matching기법을 사용하였다. Tracking을 활용하는 방식에는 동영상의 기준프레임을 활용하여 Tracking하는 방법과 각 프레임을 순차적으로 Tracking하여 비교하는 방법, 그리고 마커를 사용하지 않고 동영상의 Tracking데이터를 추출하는 방법이 있는데 본 논문에서는 이 세 가지 방법을 비교하여, 증강현실 저작도구의 상용화를 위한 최적화된 알고리즘을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.808-810
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• 2018

Pedestrian tracking is a particular problem of object tracking, and an important component in various vision-based applications, such as autonomous cars or surveillance systems. After several years of development, pedestrian tracking in videos is still a challenging problem because of various visual properties of objects and surrounding environment. In this research, we propose a tracking-by-detection system for pedestrian tracking, which incorporates Convolutional Neural Network (CNN) and color information. Pedestrians in video frames are localized by a CNN, then detected pedestrians are assigned to their corresponding tracklets based on similarities in color distributions. The experimental results show that our system was able to overcome various difficulties to produce highly accurate tracking results.