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Proceedings Paper

Acoustic semantic labeling and fusion of human-vehicle interactions
Author(s): Amir Shirkhodaie; Vinayak Elangovan; Aaron Rababaah
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Paper Abstract

Situational awareness in a Persistent Surveillance System (PSS) can be significantly improved by fusion of Data from physical (Hard) sensors and information provided by human observers (as Soft/biological sensors) from the field. One of the major limitations that this trend brings about is, however, the integration and fusion of the sensory data collected from hard sensors along with soft data gathered from human agents in a consistent and cohesive way. This paper presents a proposed approach for semantic labeling of vehicular non-stationary acoustic events in the context of PSS. Two techniques for feature extraction based on discrete wavelet and short-time Fourier transforms are described. A correlation-based classifier is proposed for classifying and semantic labeling of vehicular acoustic events. The presented result demonstrates the proposed solution is both reliable and effective, and can be extended to future PSS applications.

Paper Details

Date Published: 5 May 2011
PDF: 12 pages
Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80500Q (5 May 2011); doi: 10.1117/12.883544
Show Author Affiliations
Amir Shirkhodaie, Tennessee State Univ. (United States)
Vinayak Elangovan, Tennessee State Univ. (United States)
Aaron Rababaah, Univ. of Maryland Eastern Shore (United States)


Published in SPIE Proceedings Vol. 8050:
Signal Processing, Sensor Fusion, and Target Recognition XX
Ivan Kadar, Editor(s)

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