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DeepKalPose: An Enhanced Deep-Learning Kalman Filter for Temporally Consistent Monocular Vehicle Pose Estimation
  • Leandro Di Bella,
  • Yangxintong Lyu,
  • Adrian Munteanu
Leandro Di Bella
Vrije Universiteit Brussel

Corresponding Author:[email protected]

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Yangxintong Lyu
Vrije Universiteit Brussel
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Adrian Munteanu
Vrije Universiteit Brussel
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In this paper, we introduce an innovative temporal consistency enhancement approach, which enables image-based models on video data by leveraging a deep-learning-based Kalman Filter. More specifically, we propose a novel Bi-direction Kalman filter strategy, utilizing forward and backward processing to capitalize on higher-quality pose estimations near the camera, enhancing the robustness and precision of vehicle tracking across varying distances and conditions. Then, rather than using the conventional mathematical motion model, we propose a learnable motion model, dubbed Future State Predictor, to represent the complex, non-linear motion patterns observed in vehicles. The experimental results demonstrate that our approach enhances pose accuracy and temporal consistency, which allows us to handle the challenging occluded/distant vehicles.
24 Jan 2024Assigned to Editor
24 Jan 2024Submission Checks Completed
28 Jan 2024Reviewer(s) Assigned
06 Feb 2024Review(s) Completed, Editorial Evaluation Pending
08 Feb 2024Editorial Decision: Revise Major