Knowledge discovery in group activities through sequential observation analysis

Elangovan, Vinayak; Shirkhodaie, Amir

doi:10.1117/12.2050909

Cited by 2 publications

(1 citation statement)

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“…In the past, we have investigated methods for characterization of group activities without significant consideration of inherent physical obstructions [3][4][5][6][7][8][9]. In many FPSS applications, however, the detection, tracking, and characterization of group activities are not that simple.…”

Section: Introductionmentioning

confidence: 99%

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

Shirkhodaie

Telagamsetti

Poshtyar

et al. 2016

Signal Processing, Sensor/Information Fusion, and Target Recognition XXV

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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 InVehicle 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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