Multi-feature and Modular Pedestrian Intention Prediction using a Monocular Camera

Abstract

Accurate prediction of the intention of pedestrians to cross the path of vehicles is highly important to ensure their safety. The accuracy of these intention prediction systems is dependent on the recognition of several pedestrian-related features such as body pose, head pose, pedestrian speed, and passing direction, as well as accurate analysis of the developing traffic situation. Previous research efforts often focus only on a subset of these features, therefore producing inaccurate or incomplete results. Accordingly, this paper presents a comprehensive model for pedestrian intention prediction that incorporates the recognition of all the above features. We also adopt the Constant Velocity Model to estimate the future positions of pedestrians as early as possible. Our model includes a reasoning engine that produces a decision based on the output of the recognition systems of all the aforementioned features. We also consider occlusion scenarios that happen when multiple pedestrians are crossing simultaneously from the same or different directions. Our model is tested on well-known datasets as well as a real autonomous vehicle, and the results show high accuracy in predicting the intention of pedestrians in different scenarios, including ones with occlusion among pedestrians.