This paper presents DeepKalPose, a novel approach for enhancing temporal
consistency in monocular vehicle pose estimation applied on video through a
deep-learning-based Kalman Filter. By integrating a Bi-directional Kalman
filter strategy utilizing forward and backward time-series processing, combined
with a learnable motion model to represent complex motion patterns, our method
significantly improves pose accuracy and robustness across various conditions,
particularly for occluded or distant vehicles. Experimental validation on the
KITTI dataset confirms that DeepKalPose outperforms existing methods in both
pose accuracy and temporal consistency.

DeepKalPose 是一种通过使用基于深度学习的卡尔曼滤波器来增强视频中的单目车辆姿态估计的时间一致性的新方法，它通过整合双向卡尔曼滤波策略、可学习的运动模型以及表示复杂运动模式的方式，显著提高了在各种条件下车辆姿态的准确性和鲁棒性，特别是对于遮挡或远离的车辆。在 KITTI 数据集上的实验证实了 DeepKalPose 在姿态准确性和时间一致性方面优于现有方法。