Scene invariant crowd counting using multi-scales head detection in video surveillance

With a soaring increase in the application of video surveillance in daily life, the estimation of crowd density has already become a hot field. Crowd counting has a very close relationship with traffic planning, pedestrian analysing and emergency warning. Here, a novel crowd counting method based on multi-scales head detection is proposed. The authors' approach first uses gradients difference to extract the foreground of the images and apply the overlapped patches in different scales to split the input images. Then, the patches are selected and classified into different groups corresponding to their gradient distributions, and features are extracted for training. Finally, with the predicting result, density maps of different scales are computed and summed with the perspective map. In particular, the authors' method overcomes the traditional detecting method's deficiencies of low accuracy when facing perspective transformation. Also, experiments demonstrate that this proposed method not only achieved high accuracy in counting but also has outstanding robustness in our data sets.