Crowd Density Forecasting by Modeling Patch-based Dynamics

Abstract

Forecasting human activities observed in videos is a long-standing challenge in computer vision, which leads to various real-world applications such as mobile robots, autonomous driving, and assistive systems. In this work, we present a new visual forecasting task called crowd density forecasting. Given a video of a crowd captured by a surveillance camera, our goal is to predict how that crowd will move in future frames. To address this task, we have developed the patch-based density forecasting network (PDFN), which enables forecasting over a sequence of crowd density maps describing how crowded each location is in each video frame. PDFN represents a crowd density map based on spatially overlapping patches and learns density dynamics patch-wise in a compact latent space. This enables us to model diverse and complex crowd density dynamics efficiently, even when the input video involves a variable number of crowds that each move independently. Experimental results with several public datasets demonstrate the effectiveness of our approach compared with state-of-the-art forecasting methods.

References in corpus (9)

• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• Social Force Model for Pedestrian Dynamics
• Simple Online and Realtime Tracking
• A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation
• Recurrent World Models Facilitate Policy Evolution
• Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
• Argoverse: 3D Tracking and Forecasting with Rich Maps
• Coordinated Multi-Agent Imitation Learning
• C^3 Framework: An Open-source PyTorch Code for Crowd Counting