Unsupervised dense crowd detection by multiscale texture analysis
We propose a fully unsupervised method for the detection of dense crowds in images. We build multiscale texture-based feature vectors over the image. We use the diffusion maps algorithm to perform the clustering of our data. We propose the use of a quadtree for the coarse-graining of the diffusion map. We prove the efficiency of our method on synthetic and real images.
dense crowd multiscale diffusion maps quadtree segmentation feature extraction