Title :
AESOP: Adaptive Event detection SOftware using Programming by example
Author :
Thangali, Ashwin ; Prasad, Harsha ; Kethamakka, Sai ; Demirdjian, David ; Checka, Neal
Author_Institution :
Vecna Technol., Cambridge, MA, USA
Abstract :
This paper presents AESOP, a software tool for automatic event detection in video. AESOP employs a supervised 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.
Keywords :
hidden Markov models; learning (artificial intelligence); software packages; video signal processing; AESOP; HMM; adaptive event detection software; aerial video footage; automatic event detection; computationally efficient algorithms; event detection algorithm; event model construction; event model training; software tool; stationary camera surveillance; supervised learning approach; trajectory-based formulation; Computational modeling; Event detection; Feature extraction; Hidden Markov models; Target tracking; Training; Trajectory;
Conference_Titel :
Technologies for Homeland Security (HST), 2015 IEEE International Symposium on
Conference_Location :
Waltham, MA
Print_ISBN :
978-1-4799-1736-5
DOI :
10.1109/THS.2015.7225262