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, the 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.
Di Bella, L, Yangxintong, L & Munteanu, A 2024, 'DeepKalPose: An enhanced deep-learning Kalman filter for temporally consistent monocular vehicle pose estimation', Electronics Letters, vol. 60, no. 8, e13191, pp. 1-4. https://doi.org/10.1049/ell2.13191
Di Bella, L., Yangxintong, L., & Munteanu, A. (2024). DeepKalPose: An enhanced deep-learning Kalman filter for temporally consistent monocular vehicle pose estimation. Electronics Letters, 60(8), 1-4. Article e13191. https://doi.org/10.1049/ell2.13191
@article{51ce2e74c200491ebcf0a299922d700b,
title = "DeepKalPose: An enhanced deep-learning Kalman filter for temporally consistent monocular vehicle pose estimation",
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, the 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.",
keywords = "Artificial intelligence, Kalman Filter, Control Engineering",
author = "\{Di Bella\}, Leandro and Lyu Yangxintong and Adrian Munteanu",
note = "Publisher Copyright: {\textcopyright} 2024 The Authors. Electronics Letters published by John Wiley \& Sons Ltd on behalf of The Institution of Engineering and Technology.",
year = "2024",
month = apr,
day = "25",
doi = "10.1049/ell2.13191",
language = "English",
volume = "60",
pages = "1--4",
journal = "Electronics Letters",
issn = "0013-5194",
publisher = "Institution of Engineering and Technology",
number = "8",
}