• 한국정보통신학회논문지
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• 제11권4호
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• pp.786-792
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• 2007

비디오 감시 시스템에서 물체의 추적은 매우 중요하다. 본 논문에서는 외부 환경에서 움직이는 물체를 추적하는 방법을 제안한다. 움직이는 물체를 추적하기 위하여 먼저 가중치 차 영상을 구하여 움직이는 물체를 추출한 후 다시 닫힘 연산을 사용하여 잡음을 제거한다. 그리고 추출된 다양한 특징 정보로 매칭하여 움직이는 물체를 추적한다. 제안된 추적 방법은 가중치 차 영상을 사용하여 움직이는 물체를 추적하기에 정지된 물체가 갑자기 움직이거나 갑자기 멈출 때도 정확히 추적할 수 있다. 본 논문에서 제안한 추적 시스템은 공간위치, 형상과 명암도 특징을 종합하기에 움직이는 물체를 보다 더 효과적으로 추적할 수 있다.

• 한국멀티미디어학회논문지
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• 제18권5호
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• pp.575-582
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• 2015

Obtaining the complete trajectory of the object is a very important task in computer vision applications, such as video surveillance. Previous studies to recover the trajectory between two disconnected trajectory segments, however, do not takes into account the object's motion characteristics and uncertainty of trajectory segments. In this paper, we present a novel approach to recover the trajectory between two disjoint but associated trajectory segments, called goal-directed tracking. To incorporate the object's motion characteristics and uncertainty, the goal-directed state equation is first introduced. Then the goal-directed tracking framework is constructed by integrating the equation to the object tracking and trajectory linking process pipeline. Evaluation on challenging dataset demonstrates that proposed method can accurately recover the missing trajectory between two disconnected trajectory segments as well as appropriately constrain a motion of the object to the its goal(or the target state) with uncertainty.

• 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.

• 로봇학회논문지
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• 제6권1호
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• pp.18-26
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• 2011

This paper presents a method, Exposure Controlled Temporal Filtering (ECF), applied to visual motion tracking, that can cancel the temporal aliasing of periodic vibrations of cameras and fluctuations in illumination through the control of exposure time. We first present a theoretical analysis of the exposure induced image time integration process and how it samples sensor impingent light that is periodically fluctuating. Based on this analysis we develop a simple method to cancel high frequency vibrations that are temporally aliased onto sampled image sequences and thus to subsequent motion tracking measurements. Simulations and experiments using the 'Center of Gravity' and Normalized Cross-Correlation motion tracking methods were performed on a microscopic motion tracking system to validate the analytical predictions.

• 스마트미디어저널
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• 제5권1호
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• pp.78-87
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• 2016

The major problems of recent object tracking methods are related to the inefficient detection of moving objects due to occlusions, noisy background and inconsistent body motion. This paper presents a robust method for the detection and tracking of a moving in infrared animal videos. The tracking system is based on adaptive optical flow generation, Gaussian mixture and Kalman filtering. The adaptive Gaussian model of optical flow (GMOF) is used to extract foreground and noises are removed based on the object motion. Kalman filter enables the prediction of the object position in the presence of partial occlusions, and changes the size of the animal detected automatically along the image sequence. The presented method is evaluated in various environments of unstable background because of winds, and illuminations changes. The results show that our approach is more robust to background noises and performs better than previous methods.

• 한국멀티미디어학회논문지
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• 제25권1호
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• pp.72-81
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• 2022

Visual Object Tracking is known as the most fundamental problem in the field of computer vision. Object tracking localize the region of target object with bounding box in the video. In this paper, a custom CNN is created to extract object feature that has strong and various information. This network was constructed as a Siamese network for use as a feature extractor. The input images are passed convolution block composed of a bottleneck layers, and features are emphasized. The feature map of the target object and the search area, extracted from the Siamese network, was input as a local proposal network. Estimate the object area using the feature map. The performance of the tracking algorithm was evaluated using the OTB2013 dataset. Success Plot and Precision Plot were used as evaluation matrix. As a result of the experiment, 0.611 in Success Plot and 0.831 in Precision Plot were achieved.

• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.161-165
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• 2023

One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.

• 한국통신학회논문지
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• 제21권8호
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• pp.1885-1896
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• 1996

This paper describes the implementation of the AVT(Automatic video Tracker) using ASIC. The basic tracking algorithm is based on the spatio-temporal gradient method, and adaptive window sizing, track state decision algorithm were also realized. Newly developed ASIC performs recursive image filtering, extraction of spatio-temporal gradient/gradient functions of image in field rate. Using the FPGA/ASIC, the tracker was simply realized in one board type which can be easily applied to various image system. We conformed ASIC operation by computer simulation and tested the system in real tracking situations. From the result, the system can track the moving target which has a velocity of 2-3 pixel/field and a size of varying from 2 to 128 pixes. Also fast refresh rateof motion estimation(60Hz) improves the characteristics of servoing system which forms feedback loop with the tracker.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2002년도 춘계학술발표논문집(상)
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• pp.200-205
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• 2002

본 논문에서는 칼라 동영상에서 물체 이동에 의하여 형성된 동작영역을 확인하고, 이동방향을 추적하는 시스템을 제안한다. 비디오 동영상에서 포착된 물체의 영역을 color invariance 의 분석을 통해 추출하고, 추출된 영역에서 radial homogeneity 정도를 영역의 특징값을 추출하여 대응되는 물체 영역을 추적함으로써 물체의 제적을 확인한다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2022년도 추계학술대회
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• pp.10-13
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• 2022

최근 인공지능 기술을 바탕으로 지능형 분석을 수행하는 기계를 위한 비디오 부호화 기술의 필요성이 요구되면서, MPEG 에서는 VCM(Video Coding for Machines) 표준화를 시작하였다. VCM 에서는 기계를 위한 비디오/이미지 압축 또는 비디오/이미지 특징 압축을 위한 다양한 방법이 제시되고 있다. 본 논문에서는 객체추적(object tracking)을 위한 머신비전(machine vision) 네트워크에서 추출되는 다중스케일(multi-scale) 특징의 효율적인 압축 기법을 제시한다. 제안기법은 다중스케일 특징을 단일스케일(single-scale) 특징으로 차원을 축소하여 형성된 특징 시퀀스를 최신 비디오 코덱 표준인 VVC(Versatile Video Coding)를 사용하여 압축한다. 제안기법은 VCM 에서 제시하는 기준(anchor) 대비 89.65%의 BD-rate 부호화 성능향상을 보인다.