서지주요정보
Kalman filter based visual tracking using probabilistic motion segmentation and the first-order differential invariants = 확률적 영상분할과 일차미분불변량을 이용한 칼만필터 기반의 영상추적
서명 / 저자 Kalman filter based visual tracking using probabilistic motion segmentation and the first-order differential invariants = 확률적 영상분할과 일차미분불변량을 이용한 칼만필터 기반의 영상추적 / Joon-Woong Lee.
저자명 Lee, Joon-Woong ; 이준웅
발행사항 [대전 : 한국과학기술원, 1997].
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소장정보

등록번호

8007089

소장위치/청구기호

학술문화관(문화관) 보존서고

DADE 97001

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초록정보

Segmenting and tracking moving objects from monocular gray-level images can be normally divided into three sub-problems: feature extraction, motion and 3D pose estimation. Several previous approaches, developed especially for road scenes, have shown some limited success in their performance in outdoor environments due to dynamically changing illumination, the complexity and diversity of the scene. Therefore, most previous approaches worked only for certain particular situations and produced unexpected erroneous outputs. This thesis proposes an evidential reasoning and probabilistic representation of extracted features for robustly extracting vehicles in a road scene. Generally, an evidential reasoning finds the perceptually known evidences of a target to be in an image. Since a noisy image produces unreliable features and degrades the detection and localization, selecting image primitives, which are less sensitive to noise and well represent the evidences, is important. We overcome this problem by the probabilistic integration of image features based on maximum a posteriori probability that combines the prior and likelihood probabilities using Bayes' rule. Eventually, the elaborate segmentation embodies a successful paradigm for an accurate estimation of geometry and motion as well as 3D pose. Using observed geometric data and motion parameters, we develop a Kalman filter based tracking algorithm which recursively estimates the geometry and motion of a target. Each region representing a vehicle in an image evolves smoothly under the affine transformation assumption. By integrating the parameter estimation and image segmentation, we efficiently improve the accuracy of segmentation and tracking and minimize the effects of large motion and the abrupt motion change as well as noise. The relative motion between an observer and an object causes the image deformation which can be described by the first-order differential invariants. We obtain the image deformation and the direction of relative motion from the changes in the orientation of extracted region boundaries and in the center of gravity of extracted regions between two images. Theses two terms give rise to the time-to-contact between an observer and a target and the surface normal vector of the viewed surface. We have successfully performed the visual tracking of a toy vehicle moving on a three dimensionally shaped rail by an X-Y Cartesian manipulator and have demonstrated the accuracy of segmentation and tracking of multiple moving vehicles through experiments in a variety of road scenes.

서지기타정보

서지기타정보
청구기호 {DADE 97001
형태사항 viii, 154 p. : 삽도 ; 26 cm
언어 영어
일반주기 Appendix : A, Computing line features. - B, Basics of the Kalman filtering
저자명의 한글표기 : 이준웅
지도교수의 영문표기 : In-So Kweon
지도교수의 한글표기 : 권인소
학위논문 학위논문(박사) - 한국과학기술원 : 자동화및설계공학과,
서지주기 Reference : p. 147-151
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