The goal of this work is to use computer vision to measure crowd density in outdoor scenes. Crowd density estimation is an important task in crowd monitoring. The assessment is carried out using images of a graduation scene which illustrated variation of illumination due to textured brick surface, clothing and changes of weather. Image features were extracted using grey level dependency matrix, Minkowski fractal dimension and a new method called translation invariant orthonormal Chebyshev moments. The features were then classified into a range of density by using a self organizing map. Three different techniques were used and a comparison on the classification results investigates the best performance for measuring crowd density by vision.
On crowd density estimation for surveillance
2006-01-01
6 pages
Article (Journal)
English
crowd monitoring , video surveillance , outdoor scenes , image classification , translation invariant orthonormal Chebyshev moments , crowd density estimation , computer vision , matrix algebra , self organizing map , grey level dependency matrix , Chebyshev approximation , feature extraction , Minkowski fractal dimension , image features extraction , feature classification
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