Estimating pedestrian counts in groups
Computer Vision and Image Understanding
Machine Vision and Applications
A method for counting moving people in video surveillance videos
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Semi-supervised Elastic net for pedestrian counting
Pattern Recognition
Real-Time crowd density estimation using images
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Higher-order SVD analysis for crowd density estimation
Computer Vision and Image Understanding
Accurate pedestrian counting system based on local features
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
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The estimation of the number of people in an area under surveillance is very important for the problem of crowd monitoring. When an area reaches an occupation level greater than the projected one, people's safety can be in danger. This paper describes a new technique for crowd density estimation based on Minkowski fractal dimension. The fractal dimension has been widely used to characterize data texture in a large number of physical and biological sciences. The results of our experiments show that fractal dimension can also be used to characterize levels of people congestion in images of crowds. The proposed technique is compared with a statistical and a spectral technique, in a test study of nearly 300 images of a specific area of the Liverpool Street Railway Station, London, UK. Results obtained in this test study are presented.