Fast visual object counting via example-based density estimation
Yi Wang, Yuexian Zou · 2016
Density estimation based visual object counting (DE-VOC) methods estimate the counts of an image by integrating over its predicted density map. They perform effectively but inefficiently. This paper proposes a fast DE-VOC method but maintains its effectiveness. Essentially, the feature space of image patches from VOC can be clustered into subspaces, and the examples of each subspace can be collected to learn its embedding. Also, it is assumed that the neighborhood embeddings of image patches and their corresponding density maps generated from training images are similar. With these principles, a closed form DE-VOC algorithm is derived, where the embedding and centroid of each neighborhood are precomputed by the training samples. Consequently, the density map of a given patch is estimated by simple classification and mapping. Experimental results show that our proposed method is comparable with mainstream ones on counting accuracy while running much faster in testing phase.