  The Crossing or Not-Crossing (C/NC) problem is important to autonomous
vehicles (AVs) for safe vehicle/pedestrian interactions. However, this problem
setup often ignores pedestrians walking along the direction of the vehicles'
movement (LONG). To enhance the AVs' awareness of pedestrians behavior, we make
the first step towards extending the C/NC to the C/NC/LONG problem and
recognize them based on single body pose. In contrast, previous C/NC state
classifiers depend on multiple poses or contextual information. Our proposed
shallow 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 algorithms
that provide 2D pedestrian body pose so that it is able to function across
multiple light and weather conditions.
