Learning dynamic event descriptions in image sequences

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

Automatic detection of dynamic events in video sequences has a variety of applications including visual surveillance and monitoring, video highlight extraction, intelligent transportation systems, video summarization, and many more. Learning an accurate description of the various events in real-world scenes is challenging owing to the limited user-labeled data as well as the large variations in the pattern of the events. Pattern differences arise either due to the nature of the events themselves such as the spatio-temporal events or due to missing or ambiguous data interpretation using computer vision methods. In this work, we introduce a novel method for representing and classifying events in video sequences using reversible context-free grammars. The grammars are learned using a semi-supervised learning method. More concretely, by using the classification entropy as a heuristic cost function, the grammars are iteratively learned using a search method. Experimental results demonstrating the efficacy of the learning algorithm and the event detection method applied to traffic video sequences are presented.

Original languageEnglish (US)
Title of host publication2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
DOIs
StatePublished - Oct 11 2007
Event2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States
Duration: Jun 17 2007Jun 22 2007

Other

Other2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
CountryUnited States
CityMinneapolis, MN
Period6/17/076/22/07

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    Veeraraghavan, H., Papanikolopoulos, N. P., & Schrater, P. R. (2007). Learning dynamic event descriptions in image sequences. In 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 [4270100] https://doi.org/10.1109/CVPR.2007.383075