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Minimizing traffic accidents between vehicles and pedestrians is one of theprimary research goals in intelligent transportation systems. To achieve thegoal, pedestrian orientation recognition and prediction of pedestrian'scrossing or not-crossing intention play a central role. Contemporary approachesdo not guarantee satisfactory performance due to limited field-of-view, lack ofgeneralization, and high computational complexity. To overcome theselimitations, we propose a real-time predictive pedestrian collision warningservice (P2CWS) for two tasks: pedestrian orientation recognition (100.53 FPS)and intention prediction (35.76 FPS). Our framework obtains satisfyinggeneralization over multiple sites because of the proposed site-independentfeatures. At the center of the feature extraction lies 3D pose estimation. The3D pose analysis enables robust and accurate recognition of pedestrianorientations and prediction of intentions over multiple sites. The proposedvision framework realizes 89.3% accuracy in the behavior recognition task onthe TUD dataset without any training process and 91.28% accuracy in intentionprediction on our dataset achieving new state-of-the-art performance. Tocontribute to the corresponding research community, we make our source codespublic which are available at https://github.com/Uehwan/VisionForPedestrian

A Real-Time Predictive Pedestrian Collision Warning Service for Cooperative Intelligent Transportation Systems Using 3D Pose Estimation