Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-540-85654-2_19
Title: Tracking moving objects in anonymized trajectories
Authors: Vyahhi, N.
Bakiras, S.
Kalnis, P. 
Ghinita, G.
Issue Date: 2008
Source: Vyahhi, N.,Bakiras, S.,Kalnis, P.,Ghinita, G. (2008). Tracking moving objects in anonymized trajectories. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5181 LNCS : 158-171. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-540-85654-2_19
Abstract: Multiple target tracking (MTT) is a well-studied technique in the field of radar technology, which associates anonymized measurements with the appropriate object trajectories. This technique, however, suffers from combinatorial explosion, since each new measurement may potentially be associated with any of the existing tracks. Consequently, the complexity of existing MTT algorithms grows exponentially with the number of objects, rendering them inapplicable to large databases. In this paper, we investigate the feasibility of applying the MTT framework in the context of large trajectory databases. Given a history of object movements, where the corresponding object ids have been removed, our goal is to track the trajectory of every object in the database in successive timestamps. Our main contribution lies in the transition from an exponential solution to a polynomial one. We introduce a novel method that transforms the tracking problem into a min-cost max-flow problem. We then utilize well-known graph algorithms that work in polynomial time with respect to the number of objects. The experimental results indicate that the proposed methods produce high quality results that are comparable with the state-of-the-art MTT algorithms. In addition, our methods reduce significantly the computational cost and scale to a large number of objects. © 2008 Springer-Verlag Berlin Heidelberg.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/40597
ISBN: 3540856536
ISSN: 03029743
DOI: 10.1007/978-3-540-85654-2_19
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