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The Crossing or Not-Crossing (C/NC) problem is important to autonomousvehicles (AVs) for safe vehicle/pedestrian interactions. However, this problemsetup often ignores pedestrians walking along the direction of the vehicles'movement (LONG). To enhance the AVs' awareness of pedestrians behavior, we makethe first step towards extending the C/NC to the C/NC/LONG problem andrecognize them based on single body pose. In contrast, previous C/NC stateclassifiers depend on multiple poses or contextual information. Our proposedshallow neural network classifier aims to recognize these three states swiftly.We tested it on the JAAD dataset and reported an average 81.23% accuracy.Furthermore, this model can be integrated with different sensors and algorithmsthat provide 2D pedestrian body pose so that it is able to function acrossmultiple light and weather conditions.

Estimating Pedestrian Moving State Based on Single 2D Body Pose