@inproceedings{jiang2008abnormal,
abstract = {Clustering-based approaches for abnormal video event detection have been proven to be effective in the recent literature. Based on the framework proposed in our previous work [1], we have developed in this paper a new strategy for unsupervised trajectory clustering. More specifically, an information-based trajectory dissimilarity measure is proposed, based on the Bayesian information criterion (BIC). In order to minimize BIC, the agglomerative hierarchical clustering is applied using a 2-depth greedy search process. This strategy achieves better clustering results compared to the traditional 1-depth greedy search. The increased computational complexity is addressed with several bounds on the trajectory dissimilarity. {\textcopyright}2008 IEEE.},
author = {{Fan Jiang} and {Ying Wu} and Katsaggelos, Aggelos K. and Jiang, Fan and Wu, Ying and Katsaggelos, Aggelos K.},
booktitle = {2008 IEEE International Conference on Acoustics, Speech and Signal Processing},
doi = {10.1109/ICASSP.2008.4518063},
isbn = {978-1-4244-1483-3},
issn = {1520-6149},
keywords = {Event detection,Unsupervised clustering,Video surveillance},
month = {mar},
organization = {IEEE},
pages = {2129--2132},
publisher = {IEEE},
title = {{Abnormal event detection based on trajectory clustering by 2-depth greedy search}},
url = {http://ieeexplore.ieee.org/document/4518063/},
year = {2008}
}

{"_id":"L5LPsbBm2fnjP7jy2","bibbaseid":"fanjiang-yingwu-katsaggelos-jiang-wu-katsaggelos-abnormaleventdetectionbasedontrajectoryclusteringby2depthgreedysearch-2008","author_short":["Fan Jiang","Ying Wu","Katsaggelos, A. K.","Jiang, F.","Wu, Y.","Katsaggelos, A. K."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","abstract":"Clustering-based approaches for abnormal video event detection have been proven to be effective in the recent literature. Based on the framework proposed in our previous work [1], we have developed in this paper a new strategy for unsupervised trajectory clustering. More specifically, an information-based trajectory dissimilarity measure is proposed, based on the Bayesian information criterion (BIC). In order to minimize BIC, the agglomerative hierarchical clustering is applied using a 2-depth greedy search process. This strategy achieves better clustering results compared to the traditional 1-depth greedy search. The increased computational complexity is addressed with several bounds on the trajectory dissimilarity. ©2008 IEEE.","author":[{"firstnames":[],"propositions":[],"lastnames":["Fan Jiang"],"suffixes":[]},{"firstnames":[],"propositions":[],"lastnames":["Ying Wu"],"suffixes":[]},{"propositions":[],"lastnames":["Katsaggelos"],"firstnames":["Aggelos","K."],"suffixes":[]},{"propositions":[],"lastnames":["Jiang"],"firstnames":["Fan"],"suffixes":[]},{"propositions":[],"lastnames":["Wu"],"firstnames":["Ying"],"suffixes":[]},{"propositions":[],"lastnames":["Katsaggelos"],"firstnames":["Aggelos","K."],"suffixes":[]}],"booktitle":"2008 IEEE International Conference on Acoustics, Speech and Signal Processing","doi":"10.1109/ICASSP.2008.4518063","isbn":"978-1-4244-1483-3","issn":"1520-6149","keywords":"Event detection,Unsupervised clustering,Video surveillance","month":"mar","organization":"IEEE","pages":"2129–2132","publisher":"IEEE","title":"Abnormal event detection based on trajectory clustering by 2-depth greedy search","url":"http://ieeexplore.ieee.org/document/4518063/","year":"2008","bibtex":"@inproceedings{jiang2008abnormal,\nabstract = {Clustering-based approaches for abnormal video event detection have been proven to be effective in the recent literature. Based on the framework proposed in our previous work [1], we have developed in this paper a new strategy for unsupervised trajectory clustering. More specifically, an information-based trajectory dissimilarity measure is proposed, based on the Bayesian information criterion (BIC). In order to minimize BIC, the agglomerative hierarchical clustering is applied using a 2-depth greedy search process. This strategy achieves better clustering results compared to the traditional 1-depth greedy search. The increased computational complexity is addressed with several bounds on the trajectory dissimilarity. {\\textcopyright}2008 IEEE.},\nauthor = {{Fan Jiang} and {Ying Wu} and Katsaggelos, Aggelos K. and Jiang, Fan and Wu, Ying and Katsaggelos, Aggelos K.},\nbooktitle = {2008 IEEE International Conference on Acoustics, Speech and Signal Processing},\ndoi = {10.1109/ICASSP.2008.4518063},\nisbn = {978-1-4244-1483-3},\nissn = {1520-6149},\nkeywords = {Event detection,Unsupervised clustering,Video surveillance},\nmonth = {mar},\norganization = {IEEE},\npages = {2129--2132},\npublisher = {IEEE},\ntitle = {{Abnormal event detection based on trajectory clustering by 2-depth greedy search}},\nurl = {http://ieeexplore.ieee.org/document/4518063/},\nyear = {2008}\n}\n","author_short":["Fan Jiang","Ying Wu","Katsaggelos, A. K.","Jiang, F.","Wu, Y.","Katsaggelos, A. K."],"key":"jiang2008abnormal","id":"jiang2008abnormal","bibbaseid":"fanjiang-yingwu-katsaggelos-jiang-wu-katsaggelos-abnormaleventdetectionbasedontrajectoryclusteringby2depthgreedysearch-2008","role":"author","urls":{"Paper":"http://ieeexplore.ieee.org/document/4518063/"},"keyword":["Event detection","Unsupervised clustering","Video surveillance"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://sites.northwestern.edu/ivpl/files/2023/06/IVPL_Updated_publications-1.bib","dataSources":["XasdXLL99y5rygCmq","3gkSihZQRfAD2KBo3","t5XMbyZbtPBo4wBGS","bEpHM2CtrwW2qE8FP","teJzFLHexaz5AQW5z"],"keywords":["event detection","unsupervised clustering","video surveillance"],"search_terms":["abnormal","event","detection","based","trajectory","clustering","depth","greedy","search","fan jiang","ying wu","katsaggelos","jiang","wu","katsaggelos"],"title":"Abnormal event detection based on trajectory clustering by 2-depth greedy search","year":2008}