Multi-density map fusion network for crowd counting

Abstract In crowed scene, its hard to get the exact number of people due to the distorted perspectives, complex backgrounds, and scale changes. People in different locations have different sizes and dimensions in an image. To deal with this problem, we propose a new multi-density map fusion method to learn the mapping from the input image to the density map. Different form previous methods, our method mainly focuses on fusing different density maps information instead of fusing multi-scale feature of the same images. The major contributions are three paralleled branches and dynamic weighting strategy. First, our network employs the first ten layers of VGG16, and the network is combined with three paralleled branches. Each branch of our network extracts image information at different scales and each branch outputs a density map. Second, to ensure the quality of the final density map, we employ learnable relative weights to fuse the three density maps. Our method has been proved more robust than many state-of-art methods. Lots of experiments have been done in the ShanghaiTech, WorldExpo10, UCSD and UCF_CC_50 dataset to show the effectiveness of our proposed method.