Recognition of human-vehicle interactions in group activities via multi-attributed semantic message generation

Vinayak Elangovan; Amir Shirkhodaie

doi:10.1117/12.2181442

15 May 2015 Recognition of human-vehicle interactions in group activities via multi-attributed semantic message generation

Vinayak Elangovan, Amir Shirkhodaie

Author Affiliations +

Proceedings Volume 9499, Next-Generation Analyst III; 949909 (2015) https://doi.org/10.1117/12.2181442
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

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.

Citation Download Citation

Vinayak Elangovan and Amir Shirkhodaie "Recognition of human-vehicle interactions in group activities via multi-attributed semantic message generation", Proc. SPIE 9499, Next-Generation Analyst III, 949909 (15 May 2015); https://doi.org/10.1117/12.2181442

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