Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory Detection: A Natural Language Processing Approach
This work addresses anomalous trajectory detection for surveillance and security applications using radar data, representing an incremental advancement over prior methods.
The paper tackles the problem of detecting anomalous trajectories in roadmap-based tracking by introducing a constrained stochastic context-free grammar (CSCFG) to model spatio-temporal patterns with specific directions and road names, and presents a novel particle filtering algorithm that works with a base-level tracker. Results using simulated GMTI radar measurements show substantial improvement in target tracking accuracy.
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.