• 한국멀티미디어학회논문지
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• 제23권10호
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• pp.1236-1249
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• 2020

In a most recent object tracking research work, applying Convolutional Neural Network and Recurrent Neural Network-based strategies become relevant for resolving the noticeable challenges in it, like, occlusion, motion, object, and camera viewpoint variations, changing several targets, lighting variations. In this paper, the LSTM Network-based Tracking association method has proposed where the technique capable of real-time multi-object tracking by creating one of the useful LSTM networks that associated with tracking, which supports the long term tracking along with solving challenges. The LSTM network is a different neural network defined in Keras as a sequence of layers, where the Sequential classes would be a container for these layers. This purposing network structure builds with the integration of tracking association on Keras neural-network library. The tracking process has been associated with the LSTM Network feature learning output and obtained outstanding real-time detection and tracking performance. In this work, the main focus was learning trackable objects locations, appearance, and motion details, then predicting the feature location of objects on boxes according to their initial position. The performance of the joint object tracking system has shown that the LSTM network is more powerful and capable of working on a real-time multi-object tracking process.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.135-135
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• 2001

In recent years there have been increasing interests in real-time object tracking with image information. Since image information is affected by illumination, this paper presents the real-time object tracking method based on neural networks that have robust characteristics under various illuminations. This paper proposes three steps to track the object and the fast tracking method. In the first step the object color is extracted using neural networks. In the second step we detect the object feature information based on invariant moment. Finally the object is tracked through a shape recognition using neural networks. To achieve the fast tracking performance, we have a global search for entire image and then have tracking the object through local search when the object is recognized.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2358-2361
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• 2001

In recent years there have been increasing interests in real-time object tracking with image information. Since image information is affected by illumination, this paper presents the real-time object tracking method based on neural networks which have robust characteristics under various illuminations. This paper proposes three steps to track the object and the fast tracking method. In the first step the object color is extracted using neural networks. In the second step we detect the object feature information based on invariant moment. Finally the object is tracked through a shape recognition using neural networks. To achieve the fast tracking performance, this paper first has a global search of entire image and tracks the object through local search when the object is recognized.

• 로봇학회논문지
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• 제17권2호
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• pp.124-132
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• 2022

With growth in intelligence of mobile robots, interaction with humans is emerging as a very important issue for mobile robots and the pedestrian tracking technique following the designated person is adopted in many cases in a way that interacts with humans. Among the existing multi-object tracking techniques for pedestrian tracking, Simple Online and Realtime Tracking (SORT) is suitable for small mobile robots that require real-time processing while having limited computational performance. However, SORT fails to reflect changes in object detection values caused by the movement of the mobile robot, resulting in poor tracking performance. In order to solve this performance degradation, this paper proposes a more stable pedestrian tracking algorithm by correcting object tracking errors caused by robot movement in real time using wheel odometry information of a mobile robot and dynamically managing the survival period of the tracker that tracks the object. In addition, the experimental results show that the proposed methodology using data collected from actual mobile robots maintains real-time and has improved tracking accuracy with resistance to the movement of the mobile robot.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2003년도 추계공동학술대회
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• pp.145-156
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• 2003

This paper for real time object tracking in this treatise detect histogram analysis that is accumulation value of binary conversion density and edge information and body that move by real time use of difference Image techniques and proposed method to object tracking. Firstly, we extract edge that can reduce quantity of data keeping information about form of input image in object detection. Object is extracted by performing difference image and binarization in edge image. Area of detected object is determined by threshold value that divide sum of horizontal accumulation value about binary conversion density by value that add horizontalityㆍverticality maximum accumulation value. Object is tracked by comparing similarity with object that is detected in previous frame and present frame. As experiment result, proposed algorithm could improve the object detection speed, and could track object by real time and could track local movement.

• 한국컴퓨터정보학회논문지
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• 제22권5호
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• pp.21-28
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• 2017

As technology related to digital imaging equipment is developed and generalized, digital imaging system is used for various purposes in fields of society. The object tracking technology from digital image data in real time is one of the core technologies required in various fields such as security system and robot system. Among the existing object tracking technologies, cam shift technology is a technique of tracking an object using color information of an object. Recently, digital image data using infrared camera functions are widely used due to various demands of digital image equipment. However, the existing cam shift method can not track objects in image data without color information. Our proposed tracking algorithm tracks the object by analyzing the color if valid color information exists in the digital image data, otherwise it generates the lightness feature information and tracks the object through it. The brightness feature information is generated from the ratio information of the width and the height of the area divided by the brightness. Experimental results shows that our tracking algorithm can track objects in real time not only in general image data including color information but also in image data captured by an infrared camera.

• 한국항공운항학회지
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• 제18권2호
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• pp.16-22
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• 2010

This paper proposes the real-time moving object tracking system UAV using color information. Case of object tracking, it have studied to recognizing the moving object or moving multiple objects on the fixed camera. And it has recognized the object in the complex background environment. But, this paper implements the moving object tracking system using the pan/tilt function of the camera after the object's region extraction. To do this tracking system, firstly, it detects the moving object of RGB/HSI color model and obtains the object coordination in acquired image using the compact boundary box. Secondly, the camera origin coordination aligns to object's top&left coordination in compact boundary box. And it tracks the moving object using the pan/tilt function of camera. It is implemented by the Labview 8.6 and NI Vision Builder AI of National Instrument co. It shows the good performance of camera trace in laboratory environment.

• 방송공학회논문지
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• 제25권2호
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• pp.192-199
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• 2020

최근 딥러닝 모델을 기반으로 한 객체 추적 알고리즘의 활용도가 증가하고 있다. 영상에서의 다중 객체의 추적을 위한 시스템은 대표적으로 객체 검출 알고리즘과 객체 추적 알고리즘의 연쇄된 형태로 구성되어있다. 하지만 여러 모듈로 구성된 연쇄 형태의 시스템은 고성능 컴퓨팅 환경을 요구하며 실제 어플리케이션으로의 적용에 제한사항으로 존재한다. 본 논문에서는 위와 같은 객체 검출-추적의 연쇄 형태의 시스템에서 객체 검출 모듈의 연산 관련 프로세스를 조정하여 저성능 컴퓨팅 환경에서도 실시간 동작을 가능하게 하는 방법을 제안한다.

• 한국정밀공학회지
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• 제15권7호
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• pp.175-181
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• 1998

This paper describes realtime visual tracking system of moving object for three dimensional target using EKF(Extended Kalman Filter). We present a new realtime visual tracking using EKF algorithm and image prediction algorithm. We demonstrate the performance of these tracking algorithm through real experiment. The experimental results show the effectiveness of the EKF algorithm and image prediction algorithm for realtime tracking and estimated state value of filter, predicting the position of moving object to minimize an image processing area, and by reducing the effect by quantization noise of image.

• 한국산학기술학회논문지
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• 제7권6호
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• pp.1243-1248
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• 2006

실시간 영상에서 객체 추적은 지난 수년 동안 컴퓨터 비전과 많은 실제 응용 분야에서 관심있는 분야이다. 그러나 때때로 시스템들은 배경 잡음을 객체로 인식하여 객체를 찾지 못하였다. 이 논문에서는 실시간으로 적응하는 배경이미지를 이용하여 객체의 추출과 추척을 위한 새로운 방법을 개발하였다. 배경이미지의 잡음을 없애고 조도에 영향 받지 않는 객체를 추출하기 위하여 이 시스템은 실시간적으로 배경이미지를 갱신하여 적응적인 배경이미지를 생성한다. 이 시스템의 객체 추출은 배경이미지와 카메라로부터 입력된 이미지의 차를 이용한다. MBR(Minimum Bounding Rectangle)을 셋팅 한 후 추출된 객체의 내부점을 이용하고, 시스템은 이 MBR을 통하여 객체를 추적한다. 추가로 본 논문은 기존의 추적 알고리즘과 비교된 제안한 방법의 수행에 대한 결과를 평가했다.