@article{Fan2011,
abstract = {Compared to other anomalous video event detection approaches that analyze object trajectories only, we propose a context-aware method to detect anomalies. By tracking all moving objects in the video, three different levels of spatiotemporal contexts are considered, i.e., point anomaly of a video object, sequential anomaly of an object trajectory, and co-occurrence anomaly of multiple video objects. A hierarchical data mining approach is proposed. At each level, frequency-based analysis is performed to automatically discover regular rules of normal events. Events deviating from these rules are identified as anomalies. The proposed method is computationally efficient and can infer complex rules. Experiments on real traffic video validate that the detected video anomalies are hazardous or illegal according to traffic regulations. {\textcopyright} 2010 Elsevier Inc. All rights reserved.},
author = {Jiang, Fan and Yuan, Junsong and Tsaftaris, Sotirios A. and Katsaggelos, Aggelos K.},
doi = {10.1016/j.cviu.2010.10.008},
issn = {10773142},
journal = {Computer Vision and Image Understanding},
keywords = {Anomaly detection,Clustering,Context,Data mining,Video surveillance},
month = {mar},
number = {3},
pages = {323--333},
title = {{Anomalous video event detection using spatiotemporal context}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1077314210002390},
volume = {115},
year = {2011}
}

{"_id":"XiPLEF37eEyxFMR3v","bibbaseid":"jiang-yuan-tsaftaris-katsaggelos-anomalousvideoeventdetectionusingspatiotemporalcontext-2011","author_short":["Jiang, F.","Yuan, J.","Tsaftaris, S. A.","Katsaggelos, A. K."],"bibdata":{"bibtype":"article","type":"article","abstract":"Compared to other anomalous video event detection approaches that analyze object trajectories only, we propose a context-aware method to detect anomalies. By tracking all moving objects in the video, three different levels of spatiotemporal contexts are considered, i.e., point anomaly of a video object, sequential anomaly of an object trajectory, and co-occurrence anomaly of multiple video objects. A hierarchical data mining approach is proposed. At each level, frequency-based analysis is performed to automatically discover regular rules of normal events. Events deviating from these rules are identified as anomalies. The proposed method is computationally efficient and can infer complex rules. Experiments on real traffic video validate that the detected video anomalies are hazardous or illegal according to traffic regulations. © 2010 Elsevier Inc. All rights reserved.","author":[{"propositions":[],"lastnames":["Jiang"],"firstnames":["Fan"],"suffixes":[]},{"propositions":[],"lastnames":["Yuan"],"firstnames":["Junsong"],"suffixes":[]},{"propositions":[],"lastnames":["Tsaftaris"],"firstnames":["Sotirios","A."],"suffixes":[]},{"propositions":[],"lastnames":["Katsaggelos"],"firstnames":["Aggelos","K."],"suffixes":[]}],"doi":"10.1016/j.cviu.2010.10.008","issn":"10773142","journal":"Computer Vision and Image Understanding","keywords":"Anomaly detection,Clustering,Context,Data mining,Video surveillance","month":"mar","number":"3","pages":"323–333","title":"Anomalous video event detection using spatiotemporal context","url":"https://linkinghub.elsevier.com/retrieve/pii/S1077314210002390","volume":"115","year":"2011","bibtex":"@article{Fan2011,\nabstract = {Compared to other anomalous video event detection approaches that analyze object trajectories only, we propose a context-aware method to detect anomalies. By tracking all moving objects in the video, three different levels of spatiotemporal contexts are considered, i.e., point anomaly of a video object, sequential anomaly of an object trajectory, and co-occurrence anomaly of multiple video objects. A hierarchical data mining approach is proposed. At each level, frequency-based analysis is performed to automatically discover regular rules of normal events. Events deviating from these rules are identified as anomalies. The proposed method is computationally efficient and can infer complex rules. Experiments on real traffic video validate that the detected video anomalies are hazardous or illegal according to traffic regulations. {\\textcopyright} 2010 Elsevier Inc. All rights reserved.},\nauthor = {Jiang, Fan and Yuan, Junsong and Tsaftaris, Sotirios A. and Katsaggelos, Aggelos K.},\ndoi = {10.1016/j.cviu.2010.10.008},\nissn = {10773142},\njournal = {Computer Vision and Image Understanding},\nkeywords = {Anomaly detection,Clustering,Context,Data mining,Video surveillance},\nmonth = {mar},\nnumber = {3},\npages = {323--333},\ntitle = {{Anomalous video event detection using spatiotemporal context}},\nurl = {https://linkinghub.elsevier.com/retrieve/pii/S1077314210002390},\nvolume = {115},\nyear = {2011}\n}\n","author_short":["Jiang, F.","Yuan, J.","Tsaftaris, S. A.","Katsaggelos, A. K."],"key":"Fan2011","id":"Fan2011","bibbaseid":"jiang-yuan-tsaftaris-katsaggelos-anomalousvideoeventdetectionusingspatiotemporalcontext-2011","role":"author","urls":{"Paper":"https://linkinghub.elsevier.com/retrieve/pii/S1077314210002390"},"keyword":["Anomaly detection","Clustering","Context","Data mining","Video surveillance"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://sites.northwestern.edu/ivpl/files/2023/06/IVPL_Updated_publications-1.bib","dataSources":["KTWAakbPXLGfYseXn","ePKPjG8C6yvpk4mEK","ya2CyA73rpZseyrZ8","7Dwzbxq93HWrJEhT6","qhF8zxmGcJfvtdeAg","fvDEHD49E2ZRwE3fb","H7crv8NWhZup4d4by","DHqokWsryttGh7pJE","vRJd4wNg9HpoZSMHD","sYxQ6pxFgA59JRhxi","w2WahSbYrbcCKBDsC","XasdXLL99y5rygCmq","3gkSihZQRfAD2KBo3","t5XMbyZbtPBo4wBGS","bEpHM2CtrwW2qE8FP","teJzFLHexaz5AQW5z"],"keywords":["anomaly detection","clustering","context","data mining","video surveillance"],"search_terms":["anomalous","video","event","detection","using","spatiotemporal","context","jiang","yuan","tsaftaris","katsaggelos"],"title":"Anomalous video event detection using spatiotemporal context","year":2011}