Spatio-temporal DenseNet for real-time intent prediction of pedestrians in urban traffic environments

Abstract

Autonomous ground vehicles are increasingly finding their way into real-life applications, ranging from food/parcel delivery to self-driving vehicles. Given that, understanding the behaviours and intentions of humans are still one of the main challenges autonomous ground vehicles faced with. More specifically, when it comes to complex environments such as urban traffic scenes, inferring the intentions and actions of vulnerable road users such as pedestrians become even harder. In this paper, we address the problem of intent action prediction of pedestrians in urban traffic environments using only image sequences from a monocular RGB camera. We propose a real-time framework that can accurately detect, track and predict the intended actions of pedestrians based on a tracking-by-detection technique in conjunction with a novel spatio-temporal DenseNet model. We trained and evaluated our framework based on real data collected from urban traffic environments. Our framework has shown resilient and competitive results in comparison to other baseline approaches. Overall, we achieved an average precision score of 84.76% with a real-time performance at 20 FPS.