@inproceedings{Jiang2007,
abstract = {The clustering-based approach for detecting abnormalities in surveillance video requires the appropriate definition of similarity between events. The HMM-based similarity defined previously falls short in handling the overfitting problem. We propose in this paper a multi-sample-based similarity measure, where HMM training and distance measuring are based on multiple samples. These multiple training data are acquired by a novel dynamic hierarchical clustering (DHC) method. By iteratively reclassifying and retraining the data groups at different clustering levels, the initial training and clustering errors due to overfitting will be sequentially corrected in later steps. Experimental results on real surveillance video show an improvement of the proposed method over a baseline method that uses single-sample-based similarity measure and spectral clustering. {\textcopyright}2007 IEEE.},
author = {Jiang, Fan and Wu, Ying and Katsaggelos, Aggelos K.},
booktitle = {2007 IEEE International Conference on Image Processing},
doi = {10.1109/ICIP.2007.4379786},
isbn = {978-1-4244-1436-9},
issn = {15224880},
keywords = {Clustering,Event detection,Surveillance},
pages = {V -- 145--V -- 148},
publisher = {IEEE},
title = {{Abnormal Event Detection from Surveillance Video by Dynamic Hierarchical Clustering}},
url = {http://ieeexplore.ieee.org/document/4379786/},
volume = {5},
year = {2007}
}

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