Paper
21 May 2015 AESOP: Adaptive Event detection SOftware using Programming by example
Ashwin Thangali, Harsha Prasad, Sai Kethamakka, David Demirdjian, Neal Checka
Author Affiliations +
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ashwin Thangali, Harsha Prasad, Sai Kethamakka, David Demirdjian, and Neal Checka "AESOP: Adaptive Event detection SOftware using Programming by example", Proc. SPIE 9473, Geospatial Informatics, Fusion, and Motion Video Analytics V, 947307 (21 May 2015); https://doi.org/10.1117/12.2179194
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KEYWORDS
Detection and tracking algorithms

Video

Video surveillance

Data modeling

Cameras

Target detection

Sensors

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