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.
The authors of REF proposed the translation invariant orthonormal Chebyshev moments method for pattern recognition.
On Crowd Density Estimation for Surveillance
In International Conference on Crime Detection and Prevention
Mathematics