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The Research on Object Correspondence Technology Based on Geometric Constraint in Multi-camera Video Surveillance System

Author: ZhaoYingXue
Tutor: HuDong
School: Nanjing University of Posts and Telecommunications
Course: Signal and Information Processing
Keywords: Multi-camera video surveillance Target Matching Fundamental matrix Levenberg-Marquardt algorithm Spindle K - L expansion
CLC: TP391.41
Type: Master's thesis
Year: 2012
Downloads: 57
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In recent years, with the public security issues have become increasingly prominent, high-performance microprocessor innovation, video analysis technology continues to evolve, the video surveillance system in security, transportation, medical and other industries has been widely used. Limited vision domain but only use a single camera, it is difficult to solve the problem of object occlusion as well as multi-camera target tracking; while the use of multiple cameras and synergy between the multi-camera can expand the camera's field of vision domain, increased observation angle , comprehensive information capture moving target and help solve the problems of object occlusion, mutations in the ambient light, 3D target modeling. For this reason, based on the multi-camera collaborative intelligent video surveillance more and more related to the attention of scholars, and also the current research focus and difficulty. This paper focuses on the intelligent monitoring of multi-camera collaborative one of the key technologies - Target-matching techniques to find the correspondence between the moving target that is in a different monitor image sequence and identity markers. Should sexual constraints of this paper, the use of very geometric constraints and single both geometric constraints, research and innovations are as follows: (1) get extremely geometrical relationship, a new robust fundamental matrix estimated method. First, SIFT algorithm to extract and match feature points, the initial point of the match on the collection after pretreatment; reintegration LMedS and M-estimators method to eliminate false matching points, precise matching points on the collection; Finally, using the Levenberg-Marquardt nonlinear optimization algorithm on the basis of the matrix, precise robust fundamental matrix. The experimental results show that the estimation method: In this paper, the robustness and high accuracy. (2) homography constraint relationships to get for artificial take homography matrix inaccurate problem, this paper combined with a very geometric constraint point method based on a simple two-step take very geometric constraints. This method first artificial transfer error as a criterion the initial coordinates of the location of the matching points, and then to the pole distance criteria optimization to get more precise coordinates of the location of the matching points. (3) extraction of the spindle characteristics based LMedS method to extract spindle takes a long time, does not apply to the camera a certain extent, the tilt angle, the paper proposes a KL expansion-spindle extraction method. The method first calculating the second-order central moments, the two values ??of the target image, and thereby obtaining the target centroid and the spindle and the upper border of the angle a, and by these two parameters to determine the target spindle. (4) Based on the above studies, is presented based on the spindle and target matching method of geometric constraints (PA-EGC: Principal Axis and Epipolar Geometry Constraint). Firstly, the use of the KL expansion-style methods to extract spindle and spindle-based target match; obtain but because of the foot point, intersection calculation errors exist, the matching process may be \select target centroid point as a feature point, combined with the Epipolar geometry constraint \The experimental results show that: the spindle extraction time, relative to the literature [35] have improved matching accuracy, scene adaptability. Finally, a full-text content summary and key directions for further research on the subject outlook.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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