Fine-Grained Crowd Counting

Abstract

Current crowd counting algorithms are only concerned about the number of people in an image, which lacks low-level fine-grained information of the crowd. For many practical applications, the total number of people in an image is not as useful as the number of people in each sub-category. E.g., knowing the number of people waiting inline or browsing can help retail stores; knowing the number of people standing/sitting can help restaurants/cafeterias; knowing the number of violent/non-violent people can help police in crowd management. In this paper, we propose fine-grained crowd counting, which differentiates a crowd into categories based on the low-level behavior attributes of the individuals (e.g. standing/sitting or violent behavior) and then counts the number of people in each category. To enable research in this area, we construct a new dataset of four real-world fine-grained counting tasks: traveling direction on a sidewalk, standing or sitting, waiting in line or not, and exhibiting violent behavior or not. Since the appearance features of different crowd categories are similar, the challenge of fine-grained crowd counting is to effectively utilize contextual information to distinguish between categories. We propose a two branch architecture, consisting of a density map estimation branch and a semantic segmentation branch. We propose two refinement strategies for improving the predictions of the two branches. First, to encode contextual information, we propose feature propagation guided by the density map prediction, which eliminates the effect of background features during propagation. Second, we propose a complementary attention model to share information between the two branches. Experiment results confirm the effectiveness of our method.

References in corpus (10)

• YOLOv3: An Incremental Improvement
• Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
• Conditional Random Fields as Recurrent Neural Networks
• A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation
• NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization
• Dual Graph Convolutional Network for Semantic Segmentation
• Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid
• Top-Down Feedback for Crowd Counting Convolutional Neural Network
• Dual Path Multi-Scale Fusion Networks with Attention for Crowd Counting
• EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse

Cited by in corpus (4)

• Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark
• Crowd Localization from Gaussian Mixture Scoped Knowledge and Scoped Teacher
• Learning Discriminative Features for Crowd Counting
• Feature-aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance