Features and Regression Techniques for Crowd Density Estimation: A Comparison
Oğuzhan Kurnaz, Cemal Hanilçi · 2019
Crowd density estimation is an important problem for the security applications and it is a regression task consisting of feature extraction and regression steps. In this paper, we compare different features and regression techniques for crowd density estimation. To this end 200 images randomly selected from UCSD pedestrian dataset is used in the experiments. Experimental results show that features extracted from gray level co-occurance matrix (GLCM) gives the best performance however the selection of the regression technique depends on the performance criterion. Applying perspective normalization as a pre-processing step and feature elimination as a post-processing step considerably improve the performance.