
Proceedings Paper
Physical environment virtualization for human activities recognitionFormat | Member Price | Non-Member Price |
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Paper Abstract
Human activity recognition research relies heavily on extensive datasets to verify and validate performance of activity
recognition algorithms. However, obtaining real datasets are expensive and highly time consuming. A physics-based
virtual simulation can accelerate the development of context based human activity recognition algorithms and techniques
by generating relevant training and testing videos simulating diverse operational scenarios. In this paper, we discuss in
detail the requisite capabilities of a virtual environment to aid as a test bed for evaluating and enhancing activity
recognition algorithms. To demonstrate the numerous advantages of virtual environment development, a newly
developed virtual environment simulation modeling (VESM) environment is presented here to generate calibrated multisource
imagery datasets suitable for development and testing of recognition algorithms for context-based human
activities. The VESM environment serves as a versatile test bed to generate a vast amount of realistic data for training
and testing of sensor processing algorithms. To demonstrate the effectiveness of VESM environment, we present
various simulated scenarios and processed results to infer proper semantic annotations from the high fidelity imagery
data for human-vehicle activity recognition under different operational contexts.
Paper Details
Date Published: 22 May 2015
PDF: 12 pages
Proc. SPIE 9478, Modeling and Simulation for Defense Systems and Applications X, 94780I (22 May 2015); doi: 10.1117/12.2178547
Published in SPIE Proceedings Vol. 9478:
Modeling and Simulation for Defense Systems and Applications X
Eric J. Kelmelis, Editor(s)
PDF: 12 pages
Proc. SPIE 9478, Modeling and Simulation for Defense Systems and Applications X, 94780I (22 May 2015); doi: 10.1117/12.2178547
Show Author Affiliations
Azin Poshtkar, Tennessee State Univ. (United States)
Vinayak Elangovan, Tennessee State Univ. (United States)
Amir Shirkhodaie, Tennessee State Univ. (United States)
Vinayak Elangovan, Tennessee State Univ. (United States)
Amir Shirkhodaie, Tennessee State Univ. (United States)
Alex Chan, U.S. Army Research Lab. (United States)
Shuowen Hu, U.S. Army Research Lab. (United States)
Shuowen Hu, U.S. Army Research Lab. (United States)
Published in SPIE Proceedings Vol. 9478:
Modeling and Simulation for Defense Systems and Applications X
Eric J. Kelmelis, Editor(s)
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