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감시용 로봇의 시각을 위한 인공 신경망 기반 겹친 사람의 구분
도용태(Yongtae Do) 제어로봇시스템학회 2009 제어·로봇·시스템학회 논문지 Vol.15 No.5
In recent years the space where a robot works has been expanding to the human space unlike traditional industrial robots that work only at fixed positions apart from humans. A human in the recent situation may be the owner of a robot or the target in a robotic application. This paper deals with the latter case; when a robot vision system is employed to monitor humans for a surveillance application, each person in a scene needs to be identified. Humans, however, often move together, and occlusions between them occur frequently. Although this problem has not been seriously tackled in relevant literature, it brings difficulty into later image analysis steps such as tracking and scene understanding. In this paper, a probabilistic neural network is employed to learn the patterns of the best dividing position along the top pixels of an image region of partly occlude people. As this method uses only shape information from an image, it is simple and can be implemented in real time.
Artificial Neural Network-based Method for Stereoscopic 3D Reconstruction
Yongtae Do(도용태) 제어로봇시스템학회 2020 제어·로봇·시스템학회 논문지 Vol.26 No.3
This study proposes a three-dimensional (3D) position measurement method that uses two cameras fixed at arbitrary positions. Unlike many existing techniques, the proposed method allows a direct 3D position computation from the image coordinates. The direct image-to-space relations are redundantly obtained and can be used to make efficient 3D computations. The proposed model can be easily implemented using multilayer feedforward artificial neural networks due to directness. In our test, four small neural networks were constructed using different combinations of image coordinates as the network inputs. The most accurate network was selected. As a result, we could easily train the neural networks and obtain better results compared to existing methods.
도용태(Yongtae Do) 대한전자공학회 1992 대한전자공학회 학술대회 Vol.1992 No.10
Readings from robotic sensors are somewhat uncertain. This uncertainty problem makes it difficult to employ the sensor feedback controlled robots widely in real industrial sites. In this paper, redundant sensor fusion techniques are discussed to effectively overcome the sensor uncertainty. A weighted averaging technique is proposed under static and dynamic sensing environments. Proposed technique is tested by the experiments of stereoscopic 3d position measurements.
Vision-based Kinematic Modeling of a Worm’s Posture
Yongtae Do(도용태),Kok Kiong Tan(탄 콕 키옹) 제어로봇시스템학회 2015 제어·로봇·시스템학회 논문지 Vol.21 No.3
We present a novel method to model the body posture of a worm for vision-based automatic monitoring and analysis. The worm considered in this study is a Caenorhabditis elegans (C. elegans), which is popularly used for research in biological science and engineering. We model the posture by an open chain of a few curved or rigid line segments, in contrast to previously published approaches wherein a large number of small rigid elements are connected for the modeling. Each link segment is represented by only two parameters: an arc angle and an arc length for a curved segment, or an orientation angle and a link length for a straight line segment. Links in the proposed method can be readily related using the Denavit-Hartenberg convention due to similarities to the kinematics of an articulated manipulator. Our method was tested with real worm images, and accurate results were obtained.
도용태 ( Yongtae Do ) 한국센서학회 2020 센서학회지 Vol.29 No.3
Human detection is an important aspect in many video-based sensing and monitoring systems. Studies have been actively conducted for the automatic detection of humans in camera images, and various methods have been proposed. However, there are still problems in terms of performance and computational cost. In this paper, we describe a method for efficient human detection in the field of view of a camera, which may be static or moving, through multiple processing steps. A detection line is designated at the position where a human appears first in a sensing area, and only the one-dimensional gray pixel values of the line are monitored. If any noticeable change occurs in the detection line, corner detection and optical flow computation are performed in the vicinity of the detection line to confirm the change. When significant changes are observed in the corner numbers and optical flow vectors, the final determination of human presence in the monitoring area is performed using the Histograms of Oriented Gradients method and a Support Vector Machine. The proposed method requires processing only specific small areas of two consecutive gray images. Furthermore, this method enables operation not only in a static condition with a fixed camera, but also in a dynamic condition such as an operation using a camera attached to a moving vehicle.
김길수,도용태,Kim, Kilsu,Do, Yongtae 대한임베디드공학회 2008 대한임베디드공학회논문지 Vol.3 No.2
This paper describes a low-cost omnidirectional camera system designed for the intruder detection capability of a security robot. Moving targets on sequential images are detected first by an adaptive background subtraction technique, and the targets are identified as intruders if they fail to enter a password within a preset time. A warning message is then sent to the owner's mobile phone. The owner can check scene pictures posted by the system on the web. The system developed worked well in experiments including a situation when the indoor lighting was suddenly changed.