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Pedestrian density estimation is very useful and important under transportation environment. In this paper, we present a novel weighting scheme of “bag of visual words model” for pedestrian density estimation, which characterizes both the weight and the relative spatial arrangement aspects of all visual words in depicting an image. We firstly analyze the visual words generation process. By counting the number of images through which each visual word is clustered and computing the cluster radius of each visual word, we can give each visual word a weight. Specially, the co-occurrences of visual words are computed with respect to spatial predicates over a hierarchical spatial partitioning of an image. The representation captures both the absolute and relative spatial arrangement of the words and, through the choice and combination of the predicates, can characterize a variety of spatial relationships. We validate this hypothesis using a challenging ground truth pedestrian dataset. Our approach is shown to result in higher classification accuracy rates than a non-weighting bag-of-visual-words approach. The time used to generate the visual words of our approach is only 1/20 to 1/30 compared to the time of the traditional image feature cluster process.

Pedestrian Density Estimation by a Weighted Bag of Visual Words Model