19 March 2013 Using visual analytics model for pattern matching in surveillance data
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Abstract
In a persistent surveillance system huge amount of data is collected continuously and significant details are labeled for future references. In this paper a method to summarize video data as a result of identifying events based on these tagged information is explained, leading to concise description of behavior within a section of extended recordings. An efficient retrieval of various events thus becomes the foundation for determining a pattern in surveillance system observations, both in its extended and fragmented versions. The patterns consisting of spatiotemporal semantic contents are extracted and classified by application of video data mining on generated ontology, and can be matched based on analysts interest and rules set forth for decision making. The proposed extraction and classification method used in this paper uses query by example for retrieving similar events containing relevant features, and is carried out by data aggregation. Since structured data forms majority of surveillance information this Visual Analytics model employs KD-Tree approach to group patterns in variant space and time, thus making it convenient to identify and match any abnormal burst of pattern detected in a surveillance video. Several experimental video were presented to viewers to analyze independently and were compared with the results obtained in this paper to demonstrate the efficiency and effectiveness of the proposed technique.
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Mohammad S. Habibi, "Using visual analytics model for pattern matching in surveillance data", Proc. SPIE 8663, Video Surveillance and Transportation Imaging Applications, 86630J (19 March 2013); doi: 10.1117/12.2017760; https://doi.org/10.1117/12.2017760
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