Title:	RECOGNITION OF DYNAMICAL SITUATIONS ON THE BASIS OF FUZZY FINITE STATE MACHINES
Author(s):	Vladimir V. Deviatkov and Igor I. Lychkov
ISBN:	978-989-8533-66-1
Editors:	Yingcai Xiao and Ajith P. Abraham
Year:	2017
Edition:	Single
Keywords:	Computer vision, moving objects, time series, recognition of situations, dynamic programming
Type:	Full Paper
First Page:	103
Last Page:	109
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	Automatic recognition of situations with moving objects in videos is crucial in various application fields. Trajectory-based methods for recognition of situations with moving objects in videos include finite state machines, hidden Markov models, dynamic time warping, etc. Methods based on dynamic programming like hidden Markov models and dynamic time warping are robust under noisy conditions but require preparation of training datasets that can be difficult in case of rare situations. Methods based on finite state machines make it possible to eliminate training stage at all but lack robustness under noisy conditions. This paper introduces a novel method of situation recognition that combines advantages of finite state machines and dynamic programming. The proposed method was benchmarked on recognition of situations with moving objects in road traffic scenarios. Experimental results confirmed efficiency of the proposed method on real videos.
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