  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.
