| Autor: |
Xiuen Wu, Sien Li, Tao Wang, Ge Xu, George Papageorgiou |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Biomimetics, Vol 9, Iss 8, p 462 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2313-7673 |
| DOI: |
10.3390/biomimetics9080462 |
| Popis: |
We propose a memory-enhanced multi-stage goal-driven network (ME-MGNet) for egocentric trajectory prediction in dynamic scenes. Our key idea is to build a scene layout memory inspired by human perception in order to transfer knowledge from prior experiences to the current scenario in a top-down manner. Specifically, given a test scene, we first perform scene-level matching based on our scene layout memory to retrieve trajectories from visually similar scenes in the training data. This is followed by trajectory-level matching and memory filtering to obtain a set of goal features. In addition, a multi-stage goal generator takes these goal features and uses a backward decoder to produce several stage goals. Finally, we integrate the above steps into a conditional autoencoder and a forward decoder to produce trajectory prediction results. Experiments on three public datasets, JAAD, PIE, and KITTI, and a new egocentric trajectory prediction dataset, Fuzhou DashCam (FZDC), validate the efficacy of the proposed method. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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