DeepKalPose: An Enhanced Deep-Learning Kalman Filter for Temporally Consistent Monocular Vehicle Pose Estimation

TL;DR

DeepKalPose introduces a deep-learning Kalman filter with bi-directional processing and a learnable motion model to improve temporal consistency and accuracy in monocular vehicle pose estimation from video.

Contribution

It proposes a novel bi-directional deep-learning Kalman filter with a learnable motion model for enhanced vehicle pose estimation in videos.

Findings

Outperforms existing methods in pose accuracy

Improves temporal consistency in pose estimation

Effective on occluded and distant vehicles

Abstract

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.