• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권5호
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• pp.2346-2362
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• 2016

As one of the most important issues in computer vision and image processing, online object tracking plays a key role in numerous areas of research and in many real applications. In this study, we present a novel tracking method based on the proposed structured sparse representation model, in which the tracked object is assumed to be sparsely represented by a set of object and background templates. The contributions of this work are threefold. First, the structure information of all the candidate samples is utilized by a joint sparse representation model, where the representation coefficients of these candidates are promoted to share the same sparse patterns. This representation model can be effectively solved by the simultaneous orthogonal matching pursuit method. In addition, we develop a tracking algorithm based on the proposed representation model, a discriminative candidate selection scheme, and a simple model updating method. Finally, we conduct numerous experiments on several challenging video clips to evaluate the proposed tracker in comparison with various state-of-the-art tracking algorithms. Both qualitative and quantitative evaluations on a number of challenging video clips show that our tracker achieves better performance than the other state-of-the-art methods.

• Spatial Information Research
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• 제21권3호
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• pp.11-19
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• 2013

도로에서 발생되는 차량간 충돌사고, 교통 소통 상황, 보행자 사고 등 다양한 도로 상황을 모니터링 및 자동으로 인식하여 교통정보를 제공하거나 긴급구난 서비스를 제공하기 위한 다양한 기술이 개발되고 있다. 도로 모니터링을 통한 다양한 객체 추적 및 상황인식을 위해서는 잡음 및 겹침 등에 강인한 객체 추적 기술이 요구된다. 본 논문에서는 외부 환경에서 Background Subtraction, LK-Optical Flow, 지역 기반 히스토그램 특징의 결합을 통해 추적을 위한 몇 가지 추정 인자를 생성하고 이를 통해 변화가 있는 객체, 잡음에도 비교적 강인한 추적 방법을 제안한다. 구체적으로는 객체의 초기 움직임 정보를 검출하기 위해 옵티컬 플로우를 적용하여 컬러 정보 및 밝기 변화에 무관한 이동 정보를 측정한다. 측정된 정보를 기반으로 하여 지역 히스토그램 기반 검증을 통해 신뢰도를 판단한다. 신뢰도가 낮을 경우 배경 제거 정보와 지역 히스토그램 트래커의 정보를 혼합하여 새로운 위치를 추정한다. 실험을 통해 제안된 기법이 객체를 추적하고 있는 도중 나타날 수 있는 충돌, 새로운 특징의 등장, 크기 변화 상황에 강인하게 동작함을 제시한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.597-600
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• 1999

In this paper, we propose a fast object-tracking algorithm in a moving picture. The proposed object-tracking algorithm is based on a projection scheme. More specifically, to alleviate the computational complexities of the previous motion estimation methods, we propose to use the projected row and column 1-D image data to extract the motion information. Experimental results show that the proposed method can detect the motion of an object fairly well with reduced computational time.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(4)
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• pp.89-92
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• 2001

In this paper, we propose a new approach for tracking a moving object in moving image sequences using active contour models and optical flow. In our approach object segmentation is achieved by active contours, and object tracking is done by motion estimation based on optical flow. To get more dynamic characteristics, Lagrangian dynamics combined to the active contour models. For the optical flow computation, a method, which is based on Spatiotempo-ral Energy Models, is employed to perform robust tracking under poor environments. A prototype real tracking system has been developed and applied to a contents-based video retrieval systems.

• 융합신호처리학회논문지
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• 제15권4호
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• pp.112-118
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• 2014

본 연구는 칼라기반에서 단일 이동객체 추적을 다루고 있다. 우선 매 영상에서 이동객체 영상의 밝기 변화에 따른 추적 약점을 개선하기 위해 기존의 Camshift 알고리즘을 보완하였다. 보완된 알고리즘도 추적중인 물체와 색상이 같은 주변 물체가 존재할 경우 불안정한 추적을 보여주었는데 본 연구에서는 이를 해결하기 위해 Optical Flow기반의 KLT 알고리즘과 병합하는 방법을 제시하였다. 픽셀기반의 특징점 추적을 수행하는 KLT 알고리즘은 칼라기반의 Camshift의 단점을 보완할 수 있다. 실험 결과 제안된 병합 방법은 기존의 추적단점을 보완하였으며 추적성능이 개선됨을 실험으로 확인하였다.

• International Journal of Control, Automation, and Systems
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• 제3권3호
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• pp.444-452
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• 2005

A new scheme for a mobile robot to track and capture a moving object using camera images is proposed. The moving object is assumed to be a point-object and is projected onto an image plane to form a geometrical constraint equation that provides the position data of the object based on the kinematics of the active camera. Uncertainties in position estimation caused by the point-object assumption are compensated for using the Kalman filter. To generate the shortest time path to capture the moving object, the linear and angular velocities are estimated and utilized. In this paper, the experimental results of the tracking and capturing of a target object with the mobile robot are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권6호
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• pp.3104-3120
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• 2017

The intelligent monitoring system has been successfully applied in many fields such as: monitoring of production lines, transportation, etc. Smart surveillance systems have been developed and proven effective in some specific areas such as monitoring of human activity, traffic, etc. Most of critical application monitoring systems involve object tracking as one of the key steps. However, task of tracking of moving object is not easy. In this paper, the authors propose a method to implement human object tracking in outdoor environment based on human features in shearlet domain. The proposed method uses shearlet transform which combines the human features with context-sensitiveness in order to improve the accuracy of human tracking. The proposed algorithm not only improves the edge accuracy, but also reduces wrong positions of the object between the frames. The authors validated the proposed method by calculating Euclidean distance and Mahalanobis distance values between centre of actual object and centre of tracked object, and it has been found that the proposed method gives better result than the other recent available methods.

• 드라이브 ㆍ 컨트롤
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• 제16권2호
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• pp.22-29
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• 2019

This study presented a model predictive tracking control algorithm of autonomous truck based on object state estimation using extended Kalman filter. To design the model, the 1-layer laser scanner was used to estimate position and velocity of the object using extended Kalman filter. Based on these estimations, the desired linear path for object tracking was computed. The lateral and yaw angle errors were computed using the computed linear path and relative positions of the truck. The computed errors were used in the model predictive control algorithm to compute the optimal steering angle for object tracking. The performance evaluation was conducted on Matlab/Simulink environments using planar truck model and actual point data obtained from laser scanner. The evaluation results showed that the tracking control algorithm developed in this study can track the object reasonably based on the model predictive control algorithm based on the estimated states.

• 한국지능시스템학회논문지
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• 제14권5호
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• pp.539-544
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• 2004

본 논문에서는 CONDENSATION 알고리즘을 이용하여 입자 필터(particle filter)에 기반 한 물체 추적 알고리즘을 제안한다. 입자 필터는 조건 확률 전파 모델(Conditional Density Propagation)인 베이지안(Bayesian) 추론 규칙을 적용하는 추적구조를 갖고 있기 때문에 다른 어떤 종류의 추적 알고리즘보다 뛰어난 성능을 보인다. 논문에서는 실험 결과를 통해, 외곽(contour) 추적 입자 필터가 복잡한 환경 속에서 강인한 추적 성능을 나타냄을 증명한다.

• 디지털산업정보학회논문지
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• 제4권2호
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• pp.15-19
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• 2008

In this paper, we propose a new algorithm of image depth detection using motion estimation and object tracking. In industry, robots are used for automobile, conveyer system, etc. But, these have much necessary time. Thus, in this paper, we develop the efficient method of image depth detection based on motion estimation and object tracking.