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Proceedings Paper

Recognition of human-vehicle interactions in group activities via multi-attributed semantic message generation
Author(s): Vinayak Elangovan; Amir Shirkhodaie
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Paper Abstract

Improved Situational awareness is a vital ongoing research effort for the U.S. Homeland Security for the past recent years. Many outdoor anomalous activities involve vehicles as their primary source of transportation to and from the scene where a plot is executed. Analysis of dynamics of Human-Vehicle Interaction (HVI) helps to identify correlated patterns of activities representing potential threats. The objective of this paper is bi-folded. Primarily, we discuss a method for temporal HVI events detection and verification for generation of HVI hypotheses. To effectively recognize HVI events, a Multi-attribute Vehicle Detection and Identification technique (MVDI) for detection and classification of stationary vehicles is presented. Secondly, we describe a method for identification of pertinent anomalous behaviors through analysis of state transitions between two successively detected events. Finally, we present a technique for generation of HVI semantic messages and present our experimental results to demonstrate the effectiveness of semantic messages for discovery of HVI in group activities.

Paper Details

Date Published: 15 May 2015
PDF: 15 pages
Proc. SPIE 9499, Next-Generation Analyst III, 949909 (15 May 2015); doi: 10.1117/12.2181442
Show Author Affiliations
Vinayak Elangovan, Tennessee State Univ. (United States)
Amir Shirkhodaie, Tennessee State Univ. (United States)


Published in SPIE Proceedings Vol. 9499:
Next-Generation Analyst III
Barbara D. Broome; Timothy P. Hanratty; David L. Hall; James Llinas, Editor(s)

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