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.

翻译：本文提出DeepKalPose，一种通过基于深度学习的卡尔曼滤波器增强视频中单目车辆姿态估计时间一致性的新方法。通过集成利用正向和反向时间序列处理的双向卡尔曼滤波器策略，并结合表征复杂运动模式的可学习运动模型，本方法在多种条件下（尤其是针对遮挡或远距离车辆）显著提升了姿态估计的准确性与鲁棒性。基于KITTI数据集的实验验证表明，DeepKalPose在姿态精度和时间一致性方面均优于现有方法。