Stochastic context-free grammars for scale-dependent intent inference

Bhashyam Balaji; Mustafa Fanaswala; Vikram Krishnamurthy

doi:10.1117/12.2017905

23 May 2013 Stochastic context-free grammars for scale-dependent intent inference

Bhashyam Balaji, Mustafa Fanaswala, Vikram Krishnamurthy

Proceedings Volume 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII; 874508 (2013) https://doi.org/10.1117/12.2017905
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

The detection and tracking of surface targets using airborne radars has been extensively investigated in the literature. However, the state-of-the-art techniques in multi-target tracking do not automatically provide information that is potentially of tactical significance, such as anomalous trajectory patterns. In this paper, recent work that attempts to address this problem that is based on stochastic context-free grammars (SCFGs) is reviewed. It is shown that the production rule probabilities in SCFGs can be used to constrain sizes and orientation of target trajectories and hence lead to development of more refined syntactic trackers.

Citation Download Citation

Bhashyam Balaji, Mustafa Fanaswala, and Vikram Krishnamurthy "Stochastic context-free grammars for scale-dependent intent inference", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 874508 (23 May 2013); https://doi.org/10.1117/12.2017905

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