Long-range dismount activity classification: LODAC

Denis Garagic; Jacob Peskoe; Fang Liu; Manuel Cuevas; Andrew M. Freeman; Bradley J. Rhodes

doi:10.1117/12.2053090

17 June 2014 Long-range dismount activity classification: LODAC

Denis Garagic, Jacob Peskoe, Fang Liu, Manuel Cuevas, Andrew M. Freeman, Bradley J. Rhodes

Proceedings Volume 9079, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR V; 90790P (2014) https://doi.org/10.1117/12.2053090
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

Continuous classification of dismount types (including gender, age, ethnicity) and their activities (such as walking, running) evolving over space and time is challenging. Limited sensor resolution (often exacerbated as a function of platform standoff distance) and clutter from shadows in dense target environments, unfavorable environmental conditions, and the normal properties of real data all contribute to the challenge. The unique and innovative aspect of our approach is a synthesis of multimodal signal processing with incremental non‐parametric, hierarchical Bayesian machine learning methods to create a new kind of target classification architecture. This architecture is designed from the ground up to optimally exploit correlations among the multiple sensing modalities (multimodal data fusion) and rapidly and continuously learns (online self‐tuning) patterns of distinct classes of dismounts given little a priori information. This increases classification performance in the presence of challenges posed by anti‐access/area denial (A2/AD) sensing. To fuse multimodal features, Long-range Dismount Activity Classification (LODAC) develops a novel statistical information theoretic approach for multimodal data fusion that jointly models multimodal data (i.e., a probabilistic model for cross‐modal signal generation) and discovers the critical cross‐modal correlations by identifying components (features) with maximal mutual information (MI) which is efficiently estimated using non‐parametric entropy models. LODAC develops a generic probabilistic pattern learning and classification framework based on a new class of hierarchical Bayesian learning algorithms for efficiently discovering recurring patterns (classes of dismounts) in multiple simultaneous time series (sensor modalities) at multiple levels of feature granularity.

Citation Download Citation

Denis Garagic, Jacob Peskoe, Fang Liu, Manuel Cuevas, Andrew M. Freeman, and Bradley J. Rhodes "Long-range dismount activity classification: LODAC", Proc. SPIE 9079, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR V, 90790P (17 June 2014); https://doi.org/10.1117/12.2053090

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