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		<citationkey>AlmeidaJung:2013:ChDeHu</citationkey>
		<author>Almeida, Igor Rodrigues de,</author>
		<author>Jung, Claudio Rosito,</author>
		<affiliation>Federal University of Rio Grande do Sul</affiliation>
		<affiliation>Federal University of Rio Grande do Sul</affiliation>
		<title>Change detection in human crowds</title>
		<conferencename>Conference on Graphics, Patterns and Images, 26 (SIBGRAPI)</conferencename>
		<year>2013</year>
		<editor>Boyer, Kim,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Nedel, Luciana,</editor>
		<editor>Silva, Claudio,</editor>
		<booktitle>Proceedings</booktitle>
		<date>Aug. 5-8, 2013</date>
		<publisheraddress>Los Alamitos</publisheraddress>
		<publisher>IEEE Computer Society</publisher>
		<conferencelocation>Arequipa, Peru</conferencelocation>
		<keywords>Crowd analysis, Unusual event detection, Video surveillance.</keywords>
		<abstract>This paper presents a method to detect unusual behavior in human crowds based on histograms of velocities in world coordinates. A combination of background removal and optical flow is used to extract the global motion at each image frame, discarding small motion vectors due artifacts such as noise, non-stationary background pixels and compression issues. Using a calibrated camera, the global motion can be estimated, and it is used to build a 2D histogram containing information of speed and direction for all frames. Each frame is compared with a set of previous frames by using a histogram comparison metric, resulting in a similarity vector. This vector is then used to determine changes in the crowd behavior, also allowing a classification based on the nature of the change in time: short or long-term changes. The method was tested on publicly available datasets involving crowded scenarios.</abstract>
		<language>en</language>
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		<e-mailaddress>iralmeida@inf.ufrgs.br</e-mailaddress>
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