Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, December 1996. / "December 1996." Thesis advisor(s): Gurnam S. Gill. Includes bibliographical references (p. 45). Also available online.

Thesis (doctoral) - Universität, Bochum, 2004/2005.

Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains viii, 125 p. : ill. (some col.), maps. Includes abstract. Includes bibliographical references.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 45-46). Also available in electronic version.

Thesis (MSc)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

Thomas Kronhamn. / Bibliography: leaf 3. / "August 1984." / "...partial support provided by the Office of Naval Research under contract ONR/N00014-77-C-0532. (NR041-519)"

Objects can exhibit different dynamics at different scales, a property that isoftenexploited by visual tracking algorithms. A local dynamicmodel is typically used to extract image features that are then used as inputsto a system for tracking the entire object using a global dynamic model.Approximate local dynamicsmay be brittle---point trackers drift due to image noise and adaptivebackground models adapt to foreground objects that becomestationary---but constraints from the global model can make them more robust.We propose a probabilistic framework for incorporating globaldynamics knowledge into the local feature extraction processes.A global tracking algorithm can beformulated as a generative model and used to predict feature values thatinfluence the observation process of thefeature extractor. We combine such models in a multichain graphicalmodel framework.We show the utility of our framework for improving feature tracking and thusshapeand motion estimates in a batch factorization algorithm.We also propose an approximate filtering algorithm appropriate for onlineapplications, and demonstrate its application to problems such as backgroundsubtraction, structure from motion and articulated body tracking.

In this thesis we present LaSLAT, a sensor network algorithm thatsimultaneously localizes sensors, calibrates sensing hardware, andtracks unconstrained moving targets using only range measurementsbetween the sensors and the target. LaSLAT is based on a Bayesian filter, which updates a probabilitydistribution over the quantities of interest as measurementsarrive. The algorithm is distributable, and requires only a constantamount of space with respect to the number of measurementsincorporated. LaSLAT is easy to adapt to new types of hardware and newphysical environments due to its use of intuitive probabilitydistributions: one adaptation demonstrated in this thesis uses amixture measurement model to detect and compensate for bad acousticrange measurements due to echoes.We also present results from a centralized Java implementation ofLaSLAT on both two- and three-dimensional sensor networks in whichranges are obtained using the Cricket ranging system. LaSLAT is ableto localize sensors to within several centimeters of their groundtruth positions while recovering a range measurement bias for eachsensor and the complete trajectory of the mobile.

Thesis (Ph.D.)--University of Delaware, 2009. / Principal faculty advisor: James E. Hoffman, Dept. of Psychology. Includes bibliographical references.

Η παρούσα εργασία αναπτύσσει ένα σύστημα εντοπισμού και παρακολούθησης κατά τη διάρκεια ενός ποδοσφαιρικού αγώνα.Η μεθοδολογία που αναπτύχθηκε αφορά τρία στάδια , την αφαίρεση στατικού φόντου, την ανίχνευση των κινούμενων παικτών και τον εντοπισμό τους σε όλη τη διαδρομή τους. / In this paper a football player tracking system is analyzed and developed. The methodology used can be divided in three stages, static background subtraction, detection of moving players and tracking them throughout a football game.