• 융합신호처리학회논문지
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• 제22권1호
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• pp.30-37
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• 2021

카메라 영상을 이용한 3차원 객체 추적 기술은 증강현실 응용 분야를 위한 핵심 기술이다. 영상 분류, 객체 검출, 영상 분할과 같은 컴퓨터 비전 작업에서 CNN(Convolutional Neural Network)의 인상적인 성공에 자극 받아, 3D 객체 추적을 위한 최근의 연구는 딥러닝(deep learning)을 활용하는 데 초점을 맞추고 있다. 본 논문은 이러한 딥러닝을 활용한 3차원 객체 추적 방법들을 살펴본다. 딥러닝을 활용한 3차원 객체 추적을 위한 주요 방법들을 설명하고, 향후 연구 방향에 대해 논의한다.

• Journal of Veterinary Science
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• 제23권1호
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• pp.17.1-17.10
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• 2022

Background: Inspection of livestock farms using surveillance cameras is emerging as a means of early detection of transboundary animal disease such as African swine fever (ASF). Object tracking, a developing technology derived from object detection aims to the consistent identification of individual objects in farms. Objectives: This study was conducted as a preliminary investigation for practical application to livestock farms. With the use of a high-performance artificial intelligence (AI)-based 3D depth camera, the aim is to establish a pathway for utilizing AI models to perform advanced object tracking. Methods: Multiple crossovers by two humans will be simulated to investigate the potential of object tracking. Inspection of consistent identification will be the evidence of object tracking after crossing over. Two AI models, a fast model and an accurate model, were tested and compared with regard to their object tracking performance in 3D. Finally, the recording of pig pen was also processed with aforementioned AI model to test the possibility of 3D object detection. Results: Both AI successfully processed and provided a 3D bounding box, identification number, and distance away from camera for each individual human. The accurate detection model had better evidence than the fast detection model on 3D object tracking and showed the potential application onto pigs as a livestock. Conclusions: Preparing a custom dataset to train AI models in an appropriate farm is required for proper 3D object detection to operate object tracking for pigs at an ideal level. This will allow the farm to smoothly transit traditional methods to ASF-preventing precision livestock farming.

• 방송공학회논문지
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• 제7권3호
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• pp.248-261
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• 2002

본 논문에서는 스테레오 비젼 시스템의 3차원 정보에 의해 가변되는 윈도우 마스크와 광 BPEJTC(binary phase extraction joint transform correlator)를 이용하여 스테레오 카메라를 제어하는 새로운 3차원 물체추적 시스템을 제안하였다. 즉, 스테레오 비젼 시스템의 구성 요소에 의해 3차원 정보인 추적 물체까지의 거리 정보를 쉽게 구할 수 있고, 이 거리 정보로 윈도우 마스크를 가변 시켜 추적물체 영역을 추출할 수 있다. 이 추적물체 영역은 다음 기준영상으로 갱신하여 사용된다. 그리고 이 기준영상과 스테레오 입력 영상간에 광 BPEJTC를 실행하여 추적 물체의 위치 값을 구하고. 이 값으로 스테레오 카메라의 주시각과 팬/틸트를 제어하여 3차원 물체추적이 이루어진다. 실험 결과 제안한 알고리즘은 스테레오 입력 영상에서 배경잡음과 관계없이 추적 물체영역을 추출하여 3차원 물체추적이 가능하고, 이의 구현으로 3차원 원격작업 시스템이나 적응적인 3차원 물체 추적기 등의 구현 가능성을 제시하였다.

• 한국멀티미디어학회논문지
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• 제24권2호
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• pp.167-177
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• 2021

In the field of augmented reality, edge-based methods have been popularly used in tracking textureless 3D objects. However, edge-based methods are inherently vulnerable to cluttered backgrounds. Another way to track textureless or poorly-textured 3D objects is to directly align image intensity of 3D object between consecutive frames. Although the direct methods enable more reliable and stable tracking compared to using local features such as edges, they are more sensitive to occlusion and less accurate than the edge-based methods. Therefore, we propose a method that combines an edge-based method and a direct method to leverage the advantages from each approach. Experimental results show that the proposed method is much robust to both fast camera (or object) movements and occlusion while still working in real time at a frame rate of 18 Hz. The tracking success rate and tracking accuracy were improved by up to 84% and 1.4 pixels, respectively, compared to using the edge-based method or the direct method solely.

• 반도체디스플레이기술학회지
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• 제15권3호
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• pp.46-50
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• 2016

Recently, Depending on expectancy effect and ripple effect of augmented reality, the convergence between augmented reality and culture & arts are being actively conducted. This paper proposes a learning method for effective object tracking in 3D storytelling augmented reality in cultural properties. The proposed system is based on marker-less tracking, and there are four modules that are recognition, tracking, detecting and learning module. Recognition module is composed of SURF and LSH, and then this module generates standard object information. Tracking module tracks an object using object tracking based on reliability. This information is stored in Learning module along with learned time information. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. Also, it proposes a method for robustly implementing a 3D storytelling augmented reality in cultural properties in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권9호
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• pp.1632-1652
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• 2011

This paper considers the object tracking problem in three dimensional (3-D) space when the azimuth and elevation of the object are available from the passive acoustic sensor. The particle filtering technique can be directly applied to estimate the 3-D object location, but we propose to decompose the 3-D particle filter into the three planes' particle filters, which are individually designed for the 2-D bearings-only tracking problems. 2-D bearing information is derived from the azimuth and elevation of the object to be used for the 2-D particle filter. Two estimates of three planes' particle filters are selected based on the characterization of the acoustic sensor operation in a noisy environment. The Cramer-Rao Lower Bound of the proposed 2-D particle filter-based algorithm is derived and compared against the algorithm that is based on the direct 3-D particle filter.

• Journal of information and communication convergence engineering
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• 제10권3호
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• pp.307-314
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• 2012

This paper reports on the design of an object tracker that utilizes a family of unscented Kalman filters, one for each tracked object. This is a more efficient design than having one unscented Kalman filter for the family of all moving objects. The performance of the designed and implemented filter is demonstrated by using simulated movements, and also for object movements in 2D and 3D space.

• 한국항행학회논문지
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• 제14권2호
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• pp.272-277
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• 2010

증강현실은 실세계의 정보와 가상의 정보를 연결시키기 위한 매개체가 요구되며, 이러한 매개체를 지속적으로 추적 인식하는 기술을 필요로 한다. 이러한 기술 중에 마커를 이용한 광학 트랙킹이 주류를 이루고 있으나 마커를 부착하는 과정이 불편하고 오래 걸리므로 최근에는 마커리스 트랙킹 기법이 활발히 연구되고 있다. 본 논문은 2차원 평면 즉 동일평면상의 특징점들을 트랙킹하는 방법이 아닌3차원 객체에 대한 특징점을 추출하여 실시간으로 트랙킹하는 방법을 제안한다, SURF(Speed Up Robust Features)를 이용하여 특징점을 추출하고 이를 POSIT(Pose Object System for Iteration) 알고리즘으로 3차원 객체의 회전과 이동정보를 얻어 실시간으로 객체를 추적한다. 추적 실패시 실시간으로 재추적이 가능하도록 빠른 특징점 추출과 매칭을 통하여 트랙킹에 적합한 특징점을 선택하여 객체의 위치와 회전 정보를 얻어 객체를 실시간으로 추적 및 재표현 하였다.

• 로봇학회논문지
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• 제2권2호
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• pp.129-136
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• 2007

This paper introduces a position-based robust visual servoing method which is developed for operation of a human-like robot with two arms. The proposed visual servoing method utilizes SIFT algorithm for object detection and CAMSHIFT algorithm for object tracking. While the conventional CAMSHIFT has been used mainly for object tracking in a 2D image plane, we extend its usage for object tracking in 3D space, by combining the results of CAMSHIFT for two image plane of a stereo camera. This approach shows a robust and dependable result. Once the robot's task is defined based on the extracted 3D information, the robot is commanded to carry out the task. We conduct several position-based visual servoing tasks and compare performances under different conditions. The results show that the proposed visual tracking algorithm is simple but very effective for position-based visual servoing.

• 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.