Kohteenseurannan menetelmiä konenäön avulla

Tämä tutkielma koostuu kirjallisuuskatsauksesta kohteenseurantaan konenäön avulla. Tarkoituksena on selvittää kohteenseurantamenetelmien toimintaperiaatteita ja verrata eri kohteenseurannan ratkaisuja keskenään.Tutkielman lopussa esitellään kohteenseurannan hyvä tarkkuus ideaaliolosuhteissa, hybridiratkaisujen hyödyt ja ratkaisujen heikentynyt toiminta peitettäessä

Visual Tracking and Motion Estimation for an On-orbit Servicing of a Satellite

This thesis addresses visual tracking of a non-cooperative as well as a partially cooperative satellite, to enable close-range rendezvous between a servicer and a target satellite. Visual tracking and estimation of relative motion between a servicer and a target satellite are critical abilities for rendezvous and proximity operation such as repairing and deorbiting. For this purpose, Lidar has been widely employed in cooperative rendezvous and docking missions. Despite its robustness to harsh space illumination, Lidar has high weight and rotating parts and consumes more power, thus undermines the stringent requirements of a satellite design. On the other hand, inexpensive on-board cameras can provide an effective solution, working at a wide range of distances. However, conditions of space lighting are particularly challenging for image based tracking algorithms, because of the direct sunlight exposure, and due to the glossy surface of the satellite that creates strong reflection and image saturation, which leads to difficulties in tracking procedures. In order to address these difficulties, the relevant literature is examined in the fields of computer vision, and satellite rendezvous and docking. Two classes of problems are identified and relevant solutions, implemented on a standard computer are provided. Firstly, in the absence of a geometric model of the satellite, the thesis presents a robust feature-based method with prediction capability in case of insufficient features, relying on a point-wise motion model. Secondly, we employ a robust model-based hierarchical position localization method to handle change of image features along a range of distances, and localize an attitude-controlled (partially cooperative) satellite. Moreover, the thesis presents a pose tracking method addressing ambiguities in edge-matching, and a pose detection algorithm based on appearance model learning. For the validation of the methods, real camera images and ground truth data, generated with a laboratory tet bed similar to space conditions are used. The experimental results indicate that camera based methods provide robust and accurate tracking for the approach of malfunctioning satellites in spite of the difficulties associated with specularities and direct sunlight. Also exceptional lighting conditions associated to the sun angle are discussed, aimed at achieving fully reliable localization system in a certain mission

An efficient and robust real-time contour tracking system

In this paper we present an efficient and robust real-time system for object contour tracking in image sequences. The developed application partly relies on an optimized implementation of a state-of-the-art curve fitting algorithm, and integrates important additional features in order to achieve robustness while keeping the speed of the main estimation algorithm. An application program has been developed, which requires only a few standard libraries available on most platforms, and runs at video frame rate on a common PC with standard hardware equipment. 1 Motivation and Scope of the Present Work The general problem of object contour tracking in image sequences is an important and challenging topic in the computer vision community; as many researchers already pointed out, an advanced contour tracking technique can provide crucial information for many image understanding problems and, at the same time, allows the development of efficient and useful working applications in many fields of interest. We refer the reader to [1] for a survey of these applications and the related references. Among the currently available methodologies, a very appealing one is the Contracting Curve Density (CCD) algorithm: this method has been recently developed and presented in [3] as a state-of-the-art improvement over other advanced techniques such as the Condensation algorithm [6], and it has been shown to overperform them in many different estimation tasks. Nevertheless, its higher complexity has been initially considered as an obstacle to an effective real-time implementation, even in the simplified form named Real-Time CCD from the same authors [4], and a working online version still had to be investigated. Moreover, in order to realize an autonomous and robust tracking system, some important additional issues have t