Weakly-Supervised Scene-Specific Crowd Counting Using Real-Synthetic Hybrid Data

Abstract

Due to the domain gap between the public large-scale datasets and actual scenes, the crowd counting models trained on the common datasets have a significant performance degradation when applying in practical applications. To address the above issue, one of the solution is to label additional data from the novel scenes, which is time-consuming and impractical for multiple scenes. Another solution is to utilize domain adaptation approaches to adapt a well-trained model to novel scenes. However, most of these approaches focus on appearance adaptation while the background and the crowd distribution is not adapted. In this paper, we propose a weakly-supervised method with real-synthetic hybrid data which only requires a small portion of unlabelled real images and auto-generated synthetic labelled images for training. First, the hybrid data is generated based on background from the real scene and random distributed synthetic persons. Second, an initialized counter is trained based on the hybrid data and the crowd distribution is predicted based on the predictions on real images. Then, a better crowd counter is trained based on new hybrid data generated from updated crowd distribution. The process is iterated until convergence. Extensive experiments demonstrate the effectiveness of the proposed method.