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dc.contributor.authorPoiesi, Fabio
dc.date.accessioned2015-09-22T16:53:47Z
dc.date.available2015-09-22T16:53:47Z
dc.date.issued2014-03-17
dc.identifier.citationPoiesi, F. 2014. Multi-target tracking and performance evaluation on videos. Queen Mary University of Londonen_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/8848
dc.descriptionPhDen_US
dc.description.abstractMulti-target tracking is the process that allows the extraction of object motion patterns of interest from a scene. Motion patterns are often described through metadata representing object locations and shape information. In the first part of this thesis we discuss the state-of-the-art methods aimed at accomplishing this task on monocular views and also analyse the methods for evaluating their performance. The second part of the thesis describes our research contribution to these topics. We begin presenting a method for multi-target tracking based on track-before-detect (MTTBD) formulated as a particle filter. The novelty involves the inclusion of the target identity (ID) into the particle state, which enables the algorithm to deal with an unknown and unlimited number of targets. We propose a probabilistic model of particle birth and death based on Markov Random Fields. This model allows us to overcome the problem of the mixing of IDs of close targets. We then propose three evaluation measures that take into account target-size variations, combine accuracy and cardinality errors, quantify long-term tracking accuracy at different accuracy levels, and evaluate ID changes relative to the duration of the track in which they occur. This set of measures does not require pre-setting of parameters and allows one to holistically evaluate tracking performance in an application-independent manner. Lastly, we present a framework for multi-target localisation applied on scenes with a high density of compact objects. Candidate target locations are initially generated by extracting object features from intensity maps using an iterative method based on a gradient-climbing technique and an isocontour slicing approach. A graph-based data association method for multi-target tracking is then applied to link valid candidate target locations over time and to discard those which are spurious. This method can deal with point targets having indistinguishable appearance and unpredictable motion. MT-TBD is evaluated and compared with state-of-the-art methods on real-world surveillanceen_US
dc.description.sponsorshipThis work was supported by the EU, under the FP7 project APIDIS (ICT-216023) and the Artemis JU and TSB as part of the COPCAMS project (332913).en_US
dc.language.isoenen_US
dc.publisherQueen Mary University of Londonen_US
dc.subjectElectronic Engineeringen_US
dc.subjectMulti-target trackingen_US
dc.titleMulti-target tracking and performance evaluation on videosen_US
dc.typeThesisen_US
dc.rights.holderThe copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author


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    Theses Awarded by Queen Mary University of London

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