Real-time crowd density estimation using images
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This paper presents a technique for real-time crowd density estimation based on textures of crowd images. In this technique, the current image from a sequence of input images is classified into a crowd density class. Then, the classification is corrected by a low-pass filter based on the crowd density classification of the last n images of the input sequence. The technique obtained 73.89% of correct classification in a real-time application on a sequence of 9892 crowd images. Distributed processing was used in order to obtain real-time performance. © Springer-Verlag Berlin Heidelberg 2005.
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Classification (of information), Distributed computer systems, Image processing, Low pass filters, Real time systems, Crowd density estimation, Input sequence, Real-time performance, Parameter estimation
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Inglês
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 3804 LNCS, p. 355-362.
