Detecting people in dense crowds

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dc.contributor.author Sim, Chern-Horng
dc.contributor.author Rajmadhan, Ekambaram
dc.contributor.author Ranganath, Surendra
dc.date.accessioned 2014-03-16T14:12:17Z
dc.date.available 2014-03-16T14:12:17Z
dc.date.issued 2012-03
dc.identifier.citation Sim, C. H.; Rajmadhan, E. and Ranganath, Surendra, “Detecting people in dense crowds”, Machine Vision and Applications, DOI: 10.1007/s00138-010-0280-1, vol. 23, no. 2, pp. 243-253, Mar. 2012. en_US
dc.identifier.issn 0932-8092
dc.identifier.issn 1432-1769
dc.identifier.uri http://dx.doi.org/10.1007/s00138-010-0280-1
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/803
dc.description.abstract We propose a scheme to detect individuals in any image frame of a video sequence showing densely crowded scenes against cluttered backgrounds. The method uses only spatial information, and in an initial pass through the image a trained Viola–Jones-type local detector is used to locate individuals in the densely crowded scene. This yields a large number of false alarms. Hence, in a second step, we seek to reduce the false alarms, and propose two methods for this. In the first, color information from the initially detected windows is passed to a classifier to reduce the false alarms. This classifier consists of a cascade of boosted classifiers with Haar-like features as input and is trained with color information from local windows. In the second method, a weak perspective model of an uncalibrated camera is used to further reduce the false alarm rate while maintaining the detection rate. This is based on the size and locations of the detections in the image frame, without the use of any 3D world information. Results are presented in the form of receiver operating characteristic curves. For instance, at a 79.0% detection accuracy, the false alarm rate is 20.3%. en_US
dc.description.statementofresponsibility by Chern-Horng Sim, Ekambaram Rajmadhan and Surendra Ranganath
dc.format.extent Vol. 23, No. 2, pp. 243-253
dc.language.iso en en_US
dc.publisher Springer Link en_US
dc.subject Boosted classifiers en_US
dc.subject Dense crowds en_US
dc.subject Head detection en_US
dc.subject Outlier removal en_US
dc.subject Video surveillance en_US
dc.subject Weak perspective camera model en_US
dc.title Detecting people in dense crowds en_US
dc.type Article en_US
dc.relation.journal Machine Vision and Applications


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