Crowd counting on still images with fully convolutional network

Crowd counting on still images is very challenging due to heavy occlusions and scale variations. In this paper, we aim to develop a method that can accurately estimate the crowd count from a still image. Recently, convolutional neural networks have been shown effective in many computer vision tasks including crowd counting. To this end, we propose a fully convolutional network (FCN) architecture to map the input image of arbitrary size or resolution to its density map. In order to address the perspective and scale variation issues, Inception-like modules with multiple kernel size filters are used to capture multi-scale features, which is necessary for higher crowd counting performance. We test our model on challenging ShanghaiTech dataset, the results show that our method outperforms the state-of-the-art methods.