  Understanding the behaviors and intentions of humans are one of the main
challenges autonomous ground vehicles still 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.
