Search Results for author:
Found 3 papers, 0 papers with code
Recurrent Aligned Network for Generalized Pedestrian Trajectory Prediction
Previous studies have tried to tackle this problem by leveraging a portion of the trajectory data from the target domain to adapt the model.
Sparse Pedestrian Character Learning for Trajectory Prediction
Specifically, TSNet learns the negative-removed characters in the sparse character representation stream to improve the trajectory embedding obtained in the trajectory representation stream.
Sparse Instance Conditioned Multimodal Trajectory Prediction
Specifically, SICNet learns comprehensive sparse instances, i. e., representative points of the future trajectory, through a mask generated by a long short-term memory encoder and uses the memory mechanism to store and retrieve such sparse instances.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
