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 language||English (US)|
|Title of host publication||2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07|
|State||Published - Oct 11 2007|
|Event||2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States|
Duration: Jun 17 2007 → Jun 22 2007
|Other||2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07|
|Period||6/17/07 → 6/22/07|