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arXiv · 1611.03466

Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory Detection: A Natural Language Processing Approach

Vikram Krishnamurthy·Sijia Gao
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Abstract

The aim of syntactic tracking is to classify spatio-temporal patterns of a target's motion using natural language processing models. In this paper, we generalize earlier work by considering a constrained stochastic context free grammar (CSCFG) for modeling patterns confined to a roadmap. The constrained grammar facilitates modeling specific directions and road names in a roadmap. We present a novel particle filtering algorithm that exploits the CSCFG model for estimating the target's patterns. This meta-level algorithm operates in conjunction with a base-level tracking algorithm. Extensive numerical results using simulated ground moving target indicator (GMTI) radar measurements show substantial improvement in target tracking accuracy.

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Vikram Krishnamurthy, Sijia Gao. 2016-11-10. Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory Detection: A Natural Language Processing Approach. https://arxiv.org/abs/1611.03466

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