In-vehicle group activity modeling and simulation in sensor-based virtual environment

Amir Shirkhodaie; Durga Telagamsetti; Azin Poshtyar; Alex Chan; Shuowen Hu

doi:10.1117/12.2226029

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17 May 2016 In-vehicle group activity modeling and simulation in sensor-based virtual environment

Amir Shirkhodaie, Durga Telagamsetti, Azin Poshtyar, Alex Chan, Shuowen Hu

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Proceedings Volume 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV; 984215 (2016) https://doi.org/10.1117/12.2226029
Event: SPIE Defense + Security, 2016, Baltimore, Maryland, United States

Abstract

Human group activity recognition is a very complex and challenging task, especially for Partially Observable Group Activities (POGA) that occur in confined spaces with limited visual observability and often under severe occultation. In this paper, we present IRIS Virtual Environment Simulation Model (VESM) for the modeling and simulation of dynamic POGA. More specifically, we address sensor-based modeling and simulation of a specific category of POGA, called In-Vehicle Group Activities (IVGA). In VESM, human-alike animated characters, called humanoids, are employed to simulate complex in-vehicle group activities within the confined space of a modeled vehicle. Each articulated humanoid is kinematically modeled with comparable physical attributes and appearances that are linkable to its human counterpart. Each humanoid exhibits harmonious full-body motion - simulating human-like gestures and postures, facial impressions, and hands motions for coordinated dexterity. VESM facilitates the creation of interactive scenarios consisting of multiple humanoids with different personalities and intentions, which are capable of performing complicated human activities within the confined space inside a typical vehicle. In this paper, we demonstrate the efficiency and effectiveness of VESM in terms of its capabilities to seamlessly generate time-synchronized, multi-source, and correlated imagery datasets of IVGA, which are useful for the training and testing of multi-source full-motion video processing and annotation. Furthermore, we demonstrate full-motion video processing of such simulated scenarios under different operational contextual constraints.

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Amir Shirkhodaie, Durga Telagamsetti, Azin Poshtyar, Alex Chan, and Shuowen Hu "In-vehicle group activity modeling and simulation in sensor-based virtual environment", Proc. SPIE 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV, 984215 (17 May 2016); https://doi.org/10.1117/12.2226029

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