Computer Networks
On map-matching vehicle tracking data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
VanetMobiSim: generating realistic mobility patterns for VANETs
Proceedings of the 3rd international workshop on Vehicular ad hoc networks
Vehicular Mobility Simulation for VANETs
ANSS '07 Proceedings of the 40th Annual Simulation Symposium
Driving profile modeling and recognition based on soft computing approach
IEEE Transactions on Neural Networks
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Dynamic scene reconstruction for 3d virtual guidance
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
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Geolocation information is not only crucial in conventional crime investigation, but also increasingly important for digital forensics as it allows for the logical fusion of digital evidence that is often fragmented across disparate mobile assets. This, in turn, often requires the reconstruction of mobility patterns. However, real-time surveillance is often difficult and costly to conduct, especially in criminal scenarios where such process needs to take place clandestinely. In this paper, we consider a vehicular tracking scenario and we propose an offline post hoc vehicular trace reconstruction mechanism that can accurately reconstruct vehicular mobility traces of a target entity by fusing the corresponding available visual and radio-frequency surveillance data. The algorithm provides a probabilistic treatment to the problem of incomplete data by means of Bayesian inference. In particular, we realize that it is very likely that a reconstructed route of a target entity will contain gaps (due to missing trace data), so we try to probabilistically fill these gaps. This allows law enforcement agents to conduct off-line tracking while characterizing the quality of available evidence.