Transferring priors from virtual data for crowd counting in real world

Xiaoheng Jiang, Hao Liu, Li Zhang, Geyang Li, Mingliang Xu, Pei Lv, Bing Zhou

Abstract: In recent years, crowd counting has increasingly drawn attention due to its widespread applications in the field of computer vision. Most of the existing methods rely on datasets with scarce labeled images to train networks. They are prone to suffer from the over-fitting problem. Further, these existing datasets usually just give manually labeled annotations related to the head center position. This kind of annotation provides limited information. In this paper, we propose to exploit virtual synthetic crowd scenes to improve the performance of the counting network in the real world. Since we can obtain people masks easily in a synthetic dataset, we first learn to distinguish people from the background via a segmentation network using the synthetic data. Then we transfer the learned segmentation priors from synthetic data to real-world data. Finally, we train a density estimation network on real-world data by utilizing the obtained people masks. Our experiments on two crowd counting datasets demonstrate the effectiveness of the proposed method.