Dense crowd counting from still images with convolutional neural networks

• We propose a deep learning method for people counting.
• We provide a new crowd dataset called AHU-CROWD.
• We test our method on UCSD and UCF-CROWD and compare with the state-of-the-art.

For reasons of public security, modeling large crowd distributions for counting or density estimation has attracted significant research interests in recent years. Existing crowd counting algorithms rely on predefined features and regression to estimate the crowd size. However, most of them are constrained by such limitations: (1) they can handle crowds with a few tens individuals, but for crowds of hundreds or thousands, they can only be used to estimate the crowd density rather than the crowd count; (2) they usually rely on temporal sequence in crowd videos which is not applicable to still images. Addressing these problems, in this paper, we investigate the use of a deep-learning approach to estimate the number of individuals presented in a mid-level or high-level crowd visible in a single image. Firstly, a ConvNet structure is used to extract crowd features. Then two supervisory signals, i.e., crowd count and crowd density, are employed to learn crowd features and estimate the specific counting. We test our approach on a dataset containing 107 crowd images with 45,000 annotated humans inside, and each with head counts ranging from 58 to 2201. The efficacy of the proposed approach is demonstrated in extensive experiments by quantifying the counting performance through multiple evaluation criteria.