• 한국인터넷방송통신학회논문지
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• 제13권2호
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• pp.171-179
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• 2013

영상처리에서 인식에 관련된 기술들은 환경에 아주 많은 영향을 받게 되는데 이러한 인식률을 결정짓는 요소 중인 파라미터는 환경에 적절한 값을 얼마나 잘 선택하느냐에 따라서 인식률의 큰 차이를 보인다. 본 논문은 눈동자 추적 알고리즘이 사람이나 실험 환경의 변화에 따라 인식률이 저하되는 현상을 보완하기 위한 성능 향상 및 환경에 적응하는 시스템의 구현에 대한 방법이다. 최적의 파라미터를 얻기 위해 전 처리에 사용되는 이진화 알고리즘의 문턱값을 학습이 필요한 시기를 적절히 판단해 강화학습을 이용하여 다시 학습시켜 인식률을 향상시키는 방법을 사용했다. 실험데이터를 수집하기 위해 입력 장치는 가격이 저렴하고 일반적인 웹 카메라를 사용 하였으며 얼굴 영역에 해당하는 많은 양의 이미지를 수집하여 강화학습의 적응력을 실험하였다. 이미지의 그룹을 다양하게 변화시켜 실험한 결과 강화학습을 사용한 경우 그렇지 않은 경우에 비해 작게는 3% 많게는 14%가량의 성능이 향상됨을 확인하였다. 이렇게 성능이 향상된 눈동자 추적 시스템은 휴먼 컴퓨터 인터랙션 분야에 효과적으로 활용될 수 있을 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권11호
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• pp.5459-5473
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• 2017

Recently, a tracking algorithm called the spatial-temporal context model has been proposed to locate a target by using the contextual information around the target. This model has achieved excellent results when the target undergoes slight occlusion and appearance changes. However, the target location in the current frame is based on the location in the previous frame, which will lead to failure in the presence of fast motion because of the lack of a prediction mechanism. In addition, the spatial context model is updated frame by frame, which will undoubtedly result in drift once the target is occluded continuously. This paper proposes two improvements to solve the above two problems: First, four possible positions of the target in the current frame are predicted based on the displacement between the previous two frames, and then, we calculate four confidence maps at these four positions; the target position is located at the position that corresponds to the maximum value. Second, we propose a target reliability criterion and design an adaptive threshold to regulate the updating speed of the model. Specifically, we stop updating the model when the reliability is lower than the threshold. Experimental results show that the proposed algorithm achieves better tracking results than traditional STC and other algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.834-838
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• 1992

Tracking performance depends on the quantity of the measurement data. In the Kalman-Bucy filter and other trackers, this dependence is well understood in terms of the measurement noise covariance matrix, which specifies the uncertainty in the value of measurement inputs. In this paper, we derived approximated error covariance matrix to evaluate the dependence of target detection probability and false alarm probability in the presence of uncertainty of measurement origin.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권5호
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• pp.1166-1179
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• 2013

In wireless sensor network (WSN) environments, environmental noises are generated by, for example, small passing animals, crickets chirping or foliage blowing and will interfere target detection if the noises are higher than the sensor threshold value. For accurate tracking by acoustic WSNs, these environmental noises should be filtered out before initiating track. This paper presents the effect of environmental noises on target tracking and proposes a new algorithm for the noise mitigation in acoustic WSNs. We find that our noise mitigation algorithm works well even for targets with sensing range shorter than the sensor separation as well as with longer sensing ranges. It is also found that noise duration at each sensor affects the performance of the algorithm. A detection algorithm is also presented to account for the Doppler effect which is an important consideration for tracking higher-speed ground targets. For tracking, we use the weighted sensor position centroid to represent the target position measurement and use the Kalman filter (KF) for tracking.

• 한국컴퓨터정보학회논문지
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• 제22권1호
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• pp.1-7
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• 2017

In this paper, we propose an efficient method that is tracking an object in real time using particle filter and adaptive observation model. When tracking object, it happens object shape variation by camera or object movement in variety environments. The traditional method has an error of tracking from these variation, because it has fixed observation model about the selected object by the user in the initial frame. In order to overcome these problems, we propose a method that updates the observation model by calculating the similarity between the used observation model and the eight-way of edge model from the current position. If the similarity is higher than the threshold value, tracking the object using updated observation model to reset observation model. On the contrary to this, the algorithm which consists of a process is to maintain the used observation model. Finally, this paper demonstrates the performance of the stable tracking through comparison with the traditional method by using a number of experimental data.

• 로봇학회논문지
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• 제7권2호
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• pp.142-149
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• 2012

The camera has limitations of poor visibility in underwater environment due to the limited light source and medium noise of the environment. However, its usefulness in close range has been proved in many studies, especially for navigation. Thus, in this paper, vision-based object detection and tracking techniques using artificial objects for underwater robots have been studied. We employed template matching and mean shift algorithms for the object detection and tracking methods. Also, we propose the weighted correlation coefficient of adaptive threshold -based and color-region-aided approaches to enhance the object detection performance in various illumination conditions. The color information is incorporated into the template matched area and the features of the template are used to robustly calculate correlation coefficients. And the objects are recognized using multi-template matching approach. Finally, the water basin experiments have been conducted to demonstrate the performance of the proposed techniques using an underwater robot platform yShark made by KORDI.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2008년도 춘계학술발표대회
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• pp.92-94
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• 2008

In this paper we describe an approach for template based detection and tracking of objects by chamfer matching in real time video. Detecting and tracking of any objects is the key problem in computer vision. In our case we try for hand and head of human for detection and tracking by chamfer matching technique. Matching involves correlating the templates with the distance transformed scene and determining the locations where the mismatch is below a certain user defined threshold.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2004년도 춘계 종합학술대회 논문집
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• pp.396-400
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• 2004

본 연구에서는 단일 이동 객체를 추적할 수 있는 추적 시스템을 제안하였다. 추적 시스템은 차영상 기법을 이용하여 객체의 움직임을 추정하고, 카메라의 Pan/Tilt를 제어함으로서 이동 객체를 추적할 수 있도록 하였다. 구현된 시스템은 영상획득 및 전처리 단계, 움직임 추정 단계, 객체 추적 단계로 구성되었다. 시간 간격을 두고 획득된 두 영상에 있어 움직임 추정은 기본적으로 차영상 기법을 이용하였다. 차영상의 이진화 작업에 있어 임계값의 결정은 배경과 객체의 변화에 적응적으로 동작할 수 있는 기법을 사용하였다. 또한 객체 영역의 그룹화에 있어 블록 단위의 재귀적 레이블링 방법을 사용하여 연결성을 향상시켰다. 실험 결과 이동 객체의 움직임을 추정할 수 있었고, 추적 과정에서도 객체를 잃어버리지 않고 정상적으로 추적할 수 있었다.

• 한국군사과학기술학회지
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• 제23권3호
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• pp.204-212
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• 2020

For general target tracking works by receiving a set of measurements from sensor. However, if the SNR(Signal to Noise Ratio) is low due to small RCS(Radar Cross Section), caused by remote small targets, the target's information can be lost during signal processing. TBD(Track Before Detect) is an algorithm that performs target tracking without threshold for detection. That is, all sensor data is sent to the tracking system, which prevents the loss of the target's information by thresholding the signal intensity. On the other hand, using all sensor data inevitably leads to computational problems that can severely limit the application. In this paper, we propose an iterative Joint Integrated Probabilistic Data Association as a practical target tracking technique suitable for a low SNR multi-target environment with real time operation capability, and verify its performance through simulation studies.

• Journal of Information Processing Systems
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• 제10권2호
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• pp.283-299
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• 2014

Skin detection is used in many applications, such as face recognition, hand tracking, and human-computer interaction. There are many skin color detection algorithms that are used to extract human skin color regions that are based on the thresholding technique since it is simple and fast for computation. The efficiency of each color space depends on its robustness to the change in lighting and the ability to distinguish skin color pixels in images that have a complex background. For more accurate skin detection, we are proposing a new threshold based on RGB and YUV color spaces. The proposed approach starts by converting the RGB color space to the YUV color model. Then it separates the Y channel, which represents the intensity of the color model from the U and V channels to eliminate the effects of luminance. After that the threshold values are selected based on the testing of the boundary of skin colors with the help of the color histogram. Finally, the threshold was applied to the input image to extract skin parts. The detected skin regions were quantitatively compared to the actual skin parts in the input images to measure the accuracy and to compare the results of our threshold to the results of other's thresholds to prove the efficiency of our approach. The results of the experiment show that the proposed threshold is more robust in terms of dealing with the complex background and light conditions than others.