• Journal of information and communication convergence engineering
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• 제2권1호
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• pp.52-57
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• 2004

In this paper, we propose a panorama-based object tracking scheme for wide-view surveillance systems that can detect and track moving objects with a pan-tilt camera. A dynamic mosaic of the background is progressively integrated in a single image using the camera motion information. For the camera motion estimation, we calculate affine motion parameters for each frame sequentially with respect to its previous frame. The camera motion is robustly estimated on the background by discriminating between background and foreground regions. The modified block-based motion estimation is used to separate the background region. Each moving object is segmented by image subtraction from the mosaic background. The proposed tracking system has demonstrated good performance for several test video sequences.

• 한국항행학회논문지
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• 제5권1호
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• pp.26-36
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• 2001

시 변환 영상에서 가장 중요한 문제 중의 하나는 자동 목표 추적이다. 본 논문에서는 퍼지 예측을 이용한 이동물체의 위치예측 및 추적 기법을 제안한다. 먼저, 누적 차영상을 이용하여 물체와 배경을 분리한 다음 이동물체를 추출한다. 그리고 추출된 물체에 무게 중심법을 이용하여 물체의 중심점을 추출하고 추출된 물체에 가변 크기 탐색창을 사용하여 추적 성능을 높일 수 있는 기법을 제안한다. 또한 효율적인 물체 추적을 위한 조건으로 비선형적인 예측이 필요한데 본 논문에서는 다음 프레임에서의 물체의 위치를 예측하기 위해 퍼지 예측 방법을 제안한다. 실험을 통해 제안된 퍼지 예측 시스템이 여러 상황하에서 이동물체를 안정적으로 추적함을 보인다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2007년도 하계종합학술대회 논문집
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• pp.213-214
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• 2007

In this paper, we implement one tracking scheme based on the block matching using PowerPC system. We implement tracking algorithm uses the information from Infrared (IR) sensor for tracking object. When a occlusion occurs, the proposed algorithm predicts movements of an object using the historical tracking information and it can keep the object tracking.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.1055-1058
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• 2003

The robustness and reliability of vision algorithms is the key issue in robotic research and industrial applications. In this paper robust real time visual tracking in complex scene is considered. A common approach to increase robustness of a tracking system is the use of different model (CAD model etc.) known a priori. Also fusion or multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. Voting-based fusion of cues is adapted. In voting. a very simple or no model is used for fusion. The approach for this algorithm is tested in a 3D Cartesian robot which tracks a toy vehicle moving along 3D rail, and the Kalman filter is used to estimate the motion parameters. namely the system state vector of moving object with unknown dynamics. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• 반도체디스플레이기술학회지
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• 제16권3호
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• pp.78-81
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• 2017

Augmented Reality(AR) is the core technology of the future knowledge service industry. It is expected to be used in various fields such as medical, education, entertainment etc. Briefly, augmented reality technology is a technique in which a mapped virtual object is augmented when a real-world object is viewed through a device after mapping a real-world object and a virtual object. In this paper, we implemented object detection and tracking system, which is a key technology of augmented reality. To speed up the object tracking, the ORB algorithm, which is a lightweight algorithm compared to the detection algorithm, is applied. In addition, KNN classifier, which is a machine learning algorithm, was applied to detect a single object by learning multiple images.

• 제어로봇시스템학회논문지
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• 제20권1호
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• pp.78-86
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• 2014

This paper proposes a method of tracking an object for a person-following mobile robot by combining a monocular camera and a laser scanner, where each sensor can supplement the weaknesses of the other sensor. For human-robot interaction, a mobile robot needs to maintain a distance between a moving person and itself. Maintaining distance consists of two parts: object tracking and person-following. Object tracking consists of particle filtering and online learning using shape features which are extracted from an image. A monocular camera easily fails to track a person due to a narrow field-of-view and influence of illumination changes, and has therefore been used together with a laser scanner. After constructing the geometric relation between the differently oriented sensors, the proposed method demonstrates its robustness in tracking and following a person with a success rate of 94.7% in indoor environments with varying lighting conditions and even when a moving object is located between the robot and the person.

• Journal of Information Technology Applications and Management
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• 제13권3호
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• pp.59-74
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• 2006

According to developing Wireless Communication, not only a location based service at the outside but also a location based service at the inside was more increased socially. This paper proposes the efficient algorithm to locate a transfer object in frequent change of indoor environment using indoor location tracking system we develop ourself. Proposing algorithm in this paper can locate a transfer object using the Fingerprint, one of the Location Tracking techniques which are used in general to minimize error data between Location Tracking System and Fingerprint, using this way that corrects location data as KF apply to result data can improve accuracy of a transfer object. At last we are going to compare and analyze existing typical triangulation with proposed Indoor location tracking system to demonstrate algorithm efficiency for proposed Indoor location tracking system.

• International Journal of Control, Automation, and Systems
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• 제1권4호
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• pp.495-502
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• 2003

This paper focuses on the implementation of an efficient tracking method of a moving object using optimal representative blocks by way of a pan-tilt camera. The key idea is derived from the fact that when the image size of a moving object is shrunk in an image frame according to the distance between the mobile robot camera and the object in motion, the tracking performance of a moving object can be improved by reducing the size of representative blocks according to the object image size. Motion estimations using Edge Detection (ED) and Block-Matching Algorithm (BMA) are regularly employed to track objects by vision sensors. However, these methods often neglect the real-time vision data since these schemes suffer from heavy computational load. In this paper, a representative block able to significantly reduce the amount of data to be computed, is defined and optimized by changing the size of representative blocks according to the size of the object in the image frame in order to improve tracking performance. The proposed algorithm is verified experimentally by using a two degree-of- freedom active camera mounted on a mobile robot.

• International Journal of Internet, Broadcasting and Communication
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• 제13권2호
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• pp.156-165
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• 2021

In this paper, we propose an histogram weighted centroid based Siamese region proposal network for object tracking. The original Siamese region proposal network uses two identical artificial neural networks which take two different images as the inputs and decide whether the same object exist in both input images based on a similarity measure. However, as the Siamese network is pre-trained offline, it experiences many difficulties in the adaptation to various online environments. Therefore, in this paper we propose to incorporate the histogram weighted centroid feature into the Siamese network method to enhance the accuracy of the object tracking. The proposed method uses both the histogram information and the weighted centroid location of the top 10 color regions to decide which of the proposed region should become the next predicted object region.

• 한국멀티미디어학회논문지
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• 제19권4호
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• pp.774-779
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• 2016

The conventional methods using color signal for tracking the movement of the object require a lot of calculation and the performance is not accurate. In this paper, we propose a method to effectively track the moving objects using the depth information from a depth camera. First, it separates the background and the objects based on the depth difference in the depth of the screen. When an object is moved, the depth value of the object becomes blurred because of the phenomenon of Motion Blur. In order to solve the Motion Blur, we observe the changes in the characteristics of the object (the area of the object, the border length, the roundness, the actual size) by its velocity. The proposed algorithm was implemented in the simulation that was applied directly to the tracking of a golf ball. We can see that the estimated value of the proposed method is accurate enough to be very close to the actual measurement.