Estimating Pedestrian Crossing States Based on Single 2D Body Pose
Zixing Wang, Nikolaos Papanikolopoulos
Abstract
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 pedestrian 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.
Topics & Concepts
PedestrianComputer scienceArtificial intelligencePedestrian crossingComputer visionClassifier (UML)EngineeringTransport engineeringAutonomous Vehicle Technology and SafetyTraffic and Road SafetyVideo Surveillance and Tracking Methods