  Minimizing traffic accidents between vehicles and pedestrians is one of the
primary research goals in intelligent transportation systems. To achieve the
goal, pedestrian orientation recognition and prediction of pedestrian's
crossing or not-crossing intention play a central role. Contemporary approaches
do not guarantee satisfactory performance due to limited field-of-view, lack of
generalization, and high computational complexity. To overcome these
limitations, we propose a real-time predictive pedestrian collision warning
service (P2CWS) for two tasks: pedestrian orientation recognition (100.53 FPS)
and intention prediction (35.76 FPS). Our framework obtains satisfying
generalization over multiple sites because of the proposed site-independent
features. At the center of the feature extraction lies 3D pose estimation. The
3D pose analysis enables robust and accurate recognition of pedestrian
orientations and prediction of intentions over multiple sites. The proposed
vision framework realizes 89.3% accuracy in the behavior recognition task on
the TUD dataset without any training process and 91.28% accuracy in intention
prediction on our dataset achieving new state-of-the-art performance. To
contribute to the corresponding research community, we make our source codes
public which are available at https://github.com/Uehwan/VisionForPedestrian
