SILA: An Incremental Learning Approach for Pedestrian Trajectory Prediction
Name
Habibi_SILA_An_Incremental_Learning_Approach_for_Pedestrian_Trajectory_Prediction_CVPRW_2020_paper.pdf
Description
Accepted version
Size
951.42 KB
Format
Adobe PDF
Checksum (MD5)
59124d9e75e5b7a06e696b3345f49d5e
Author(s)
Habibi, Golnaz
Jaipuria, Nikita
How, Jonathan P.
Date Issued
June 2020
Journal
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Publisher
IEEE
Citation
Habibi, Golnaz, Jaipuria, Nikita and How, Jonathan P. 2020. "SILA: An Incremental Learning Approach for Pedestrian Trajectory Prediction." IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2020-June.
Version
Author's final manuscript
Abstract
© 2020 IEEE. The prediction of pedestrian motion is challenging, especially in crowded roads and intersections. Most of the current approaches apply offline methods to learn motion behaviors, but as a result, they are not able to learn continuously and typically do not generalize well to new environments. This paper presents Similarity-based Incremental Learning Algorithm (SILA) for pedestrian motion prediction with the ability of improving the learned model over the time as data is obtained incrementally. To keep the model size efficient, the motion primitives learned from the new data are compared with the previously known ones, and similar motion primitives are fused while novel motion primitives are added to the model. Results show that the SILA model growth rate is about 1/3 that of an incremental approach that does not fuse motion primitives. SILA is evaluated on different datasets and scenarios including intersections and busy streets. The results show that, even though SILA learns incrementally, it performs comparably to (and sometimes outperforms) state-of-the-art algorithms in pedestrian prediction. Additionally, SILA learning time only depends on the size of the data added incrementally, which makes SILA more efficient in terms of time and space compared to batch learning.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/cvprw50498.2020.00520
