TrajMamba：一种基于自身运动的Mamba模型，用于从第一人称视角预测行人轨迹
TrajMamba: An Ego-Motion-Guided Mamba Model for Pedestrian Trajectory Prediction from an Egocentric Perspective
AI总结：
本文提出基于Mamba模型的TrajMamba模型，通过提取行人和自身运动特征，结合自身运动引导解码器预测未来轨迹，实现在自动驾驶和机器人导航中的高性能表现。
摘要：
从第一人称视角预测跟踪行人未来轨迹是自动驾驶和机器人导航中的关键任务。该任务的挑战在于行人与自身相机之间的复杂动态相对运动。为此，本文提出了一种基于Mamba模型的自身运动引导轨迹预测网络。首先，使用两个Mamba模型作为编码器，分别从行人运动和自身车辆运动中提取行人运动和自身运动特征。然后，一个自身运动引导的Mamba解码器通过整合行人运动特征作为历史上下文与自身运动特征作为引导线索，显式建模行人与车辆之间的相对运动，以捕捉解码特征。最后，从对应未来时间戳的解码特征生成未来轨迹。大量实验表明，所提模型在PIE和JAAD数据集上实现了最先进的性能。
英文摘要：
Future trajectory prediction of a tracked pedestrian from an egocentric perspective is a key task in areas such as autonomous driving and robot navigation. The challenge of this task lies in the complex dynamic relative motion between the ego-camera and the tracked pedestrian. To address this challenge, we propose an ego-motion-guided trajectory prediction network based on the Mamba model. Firstly, two Mamba models are used as encoders to extract pedestrian motion and ego-motion features from pedestrian movement and ego-vehicle movement, respectively. Then, an ego-motion guided Mamba decoder that explicitly models the relative motion between the pedestrian and the vehicle by integrating pedestrian motion features as historical context with ego-motion features as guiding cues to capture decoded features. Finally, the future trajectory is generated from the decoded features corresponding to the future timestamps. Extensive experiments demonstrate the effectiveness of the proposed model, which achieves state-of-the-art performance on the PIE and JAAD datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
