Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction

Understand

Reasoning over visual data is a desirable capability for robotics and vision-based applications.

• Such reasoning enables forecasting of the next events or actions in videos.
• In recent years, various models have been developed based on convolution operations for prediction or forecasting, but they lack the ability to reason over spatiotemporal data and infer the relationships of different objects in the scene.
• In this paper, we present a framework based on graph convolution to uncover the spatiotemporal relationships in the scene for reasoning about pedestrian intent.