Learning from Synthetic Data for Crowd Counting in the Wild

Abstract

Recently, counting the number of people for crowd scenes is a hot topic because of its widespread applications (e.g. video surveillance, public security). It is a difficult task in the wild: changeable environment, large-range number of people cause the current methods can not work well. In addition, due to the scarce data, many methods suffer from over-fitting to a different extent. To remedy the above two problems, firstly, we develop a data collector and labeler, which can generate the synthetic crowd scenes and simultaneously annotate them without any manpower. Based on it, we build a large-scale, diverse synthetic dataset. Secondly, we propose two schemes that exploit the synthetic data to boost the performance of crowd counting in the wild: 1) pretrain a crowd counter on the synthetic data, then finetune it using the real data, which significantly prompts the model's performance on real data; 2) propose a crowd counting method via domain adaptation, which can free humans from heavy data annotations. Extensive experiments show that the first method achieves the state-of-the-art performance on four real datasets, and the second outperforms our baselines. The dataset and source code are available at https://gjy3035.github.io/GCC-CL/.

Accepted by CVPR2019

References in corpus (6)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds
• Crowd Counting using Deep Recurrent Spatial-Aware Network
• Iterative Crowd Counting
• Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
• Domain Adaptation through Synthesis for Unsupervised Person Re-identification

Cited by in corpus (9)

• Crowd counting with crowd attention convolutional neural network
• Feature-aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance
• Content-aware Density Map for Crowd Counting and Density Estimation
• Informative Scene Decomposition for Crowd Analysis, Comparison and Simulation Guidance
• Motion-guided Non-local Spatial-Temporal Network for Video Crowd Counting
• 3D Crowd Counting via Geometric Attention-guided Multi-View Fusion
• Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs
• VersatileGait: A Large-Scale Synthetic Gait Dataset Towards in-the-Wild Simulation
• Recurrent Distillation based Crowd Counting