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.
|Original language||English (US)|
|Title of host publication||2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Oct 24 2020|
|Event||2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020 - Las Vegas, United States|
Duration: Oct 24 2020 → Jan 24 2021
|Name||IEEE International Conference on Intelligent Robots and Systems|
|Conference||2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020|
|Period||10/24/20 → 1/24/21|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT The authors would like to thank all the members of the Center for Distributed Robotics Laboratory for their help. This material is based upon work partially supported by the Minnesota Robotics Institute (MnRI) and the National Science Foundation through grants #CNS-1439728 and #CNS-1939033.
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