Detection and explanation of anomalous activities: representing activities as bags of event n-grams

• Published in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 1031 - 1038 vol. 1
• Main Authors: Hamid, R., Johnson, A., Batta, S., Bobick, A., Isbell, C., Coleman, G.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Dictionaries; Educational institutions; Event detection; Information analysis; Information theory; Natural language processing; Packaging; Statistical analysis; Statistics; Streaming media
• ISBN: 0769523722; 9780769523729
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2005.127

We present a novel representation and method for detecting and explaining anomalous activities in a video stream. Drawing from natural language processing, we introduce a representation of activities as bags of event n-grams, where we analyze the global structural information of activities using their local event statistics. We demonstrate how maximal cliques in an undirected edge-weighted graph of activities, can be used in an unsupervised manner, to discover regular sub-classes of an activity class. Based on these discovered sub-classes, we formulate a definition of anomalous activities and present a way to detect them. Finally, we characterize each discovered sub-class in terms of its "most representative member" and present an information-theoretic method to explain the detected anomalies in a human-interpretable form.