Core Concepts
Introducing a Recurrent Aligned Network for generalized pedestrian trajectory prediction to address domain shift challenges.
Abstract
The article discusses the challenges of pedestrian trajectory prediction due to domain shifts and introduces a Recurrent Aligned Network (RAN) to tackle these issues. The RAN aims to align trajectory feature spaces at both time-state and time-sequence levels through a recurrent alignment module. The article highlights the importance of considering social interactions in the alignment process and presents experimental results demonstrating the superior generalization capabilities of the proposed method.
Directory:
- Introduction
- Pedestrian trajectory prediction is crucial in various applications.
- Challenges due to domain shift problem.
- Related Works
- Overview of pedestrian trajectory prediction challenges and existing methods.
- Our Method
- Introduction of Recurrent Aligned Network (RAN) for generalized pedestrian trajectory prediction.
- Description of recurrent alignment module and pre-aligned representation module.
- Experiments and Discussions
- Evaluation on three benchmark datasets: ETH-UCY, SDD, and NBA.
- Comparison with state-of-the-art methods in terms of ADE and FDE.
- Ablation Study
- Evaluation of different components of the proposed method.
- Impact of recurrent alignment loss and alignment approaches.
- Qualitative Analysis
- Visualization of feature spaces and predicted trajectories.
- Conclusion
- Summary of the contributions and future work.
Stats
이 논문은 ETH-UCY, SDD 및 NBA 데이터셋에서 실험을 수행하였습니다.
RAN은 recurrent alignment module을 통해 domain gap을 최소화합니다.
Quotes
"Our method achieves the best performance in both ADE and FDE."
"The experimental results demonstrate the superior generalization capability of our method."