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Title: Forensic tracking and surveillance : algorithms for homogeneous and heterogeneous settings
Author: Al-Kuwari, Saif
ISNI:       0000 0004 2717 748X
Awarding Body: Royal Holloway, University of London
Current Institution: Royal Holloway, University of London
Date of Award: 2011
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Digital forensics is an emerging field that has uniquely brought together academics, practitioners and law enforcement. Research in this area was inspired by the numerous challenges posed by the increased sophistication of criminal tools. Traditionally, digital forensics has been confined to the extraction of digital evidence from electronic devices. This direct extraction of digital evidence, however, no longer suffices. Indeed, extracting completely raw data without further processing and/or filtering is, in some cases, useless. These problems can be tackled by the so-called ``computational forensics" where the reconstructs evidence are undertaken further processing. One important application of computational forensics is criminal tracking, which we collectively call ``forensic tracking" and is the main subject of this thesis. This thesis adopts an algorithmic approach to investigate the feasibility of conducting forensic tracking in various environments and settings. Unlike conventional tracking, forensic tracking has to be passive such that the target (who is usually a suspect) should not be aware of the tracking process. We begin by adopting pedestrian setting and propose several online (real-time) forensic tracking algorithms to track a single or multiple targets passively. Beside the core tracking algorithms, we also propose other auxiliary algorithms to improve the robustness and resilience of tracking. We then extend the scope and consider vehicular forensic tracking, where we investigate both online and offline tracking. In online vehicular tracking, we also propose algorithms for motion prediction to estimate the near future movement of target vehicles. Offline vehicular tracking, on the other hand, entails the post-hoc extraction and probabilistic reconstruction of vehicular traces, which we adopt Bayesian approach for. Finally, the contributions of the thesis concludes with building an algorithmic solution for multi-modal tracking, which is a mixed environment combining both pedestrian and vehicular settings.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available