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Proceedings Paper

AESOP: Adaptive Event detection SOftware using Programming by example
Author(s): Ashwin Thangali; Harsha Prasad; Sai Kethamakka; David Demirdjian; Neal Checka
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Paper Abstract

This paper presents AESOP, a software tool for automatic event detection in video. AESOP employs a super- vised learning approach for constructing event models, given training examples from different event classes. A trajectory-based formulation is used for modeling events with an aim towards incorporating invariance to changes in the camera location and orientation parameters. The proposed formulation is designed to accommodate events that involve interactions between two or more entities over an extended period of time. AESOP's event models are formulated as HMMs to improve the event detection algorithm's robustness to noise in input data and to achieve computationally efficient algorithms for event model training and event detection. AESOP's performance is demonstrated on a wide range of different scenarios, including stationary camera surveillance and aerial video footage captured in land and maritime environments.

Paper Details

Date Published: 21 May 2015
PDF: 15 pages
Proc. SPIE 9473, Geospatial Informatics, Fusion, and Motion Video Analytics V, 947307 (21 May 2015); doi: 10.1117/12.2179194
Show Author Affiliations
Ashwin Thangali, Vecna Technologies, Inc. (United States)
Harsha Prasad, Vecna Technologies, Inc. (United States)
Sai Kethamakka, Vecna Technologies, Inc. (United States)
David Demirdjian, Vecna Technologies, Inc. (United States)
Neal Checka, Vecna Technologies, Inc. (United States)

Published in SPIE Proceedings Vol. 9473:
Geospatial Informatics, Fusion, and Motion Video Analytics V
Matthew F. Pellechia; Kannappan Palaniappan; Peter J. Doucette; Shiloh L. Dockstader; Gunasekaran Seetharaman; Paul B. Deignan, Editor(s)

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