Matching and recovering 3D people from multiple views
10.1109/WACV51458.2022.00125
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/367127
Tipus de documentText en actes de congrés
Data publicació2022
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
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Abstract
This paper introduces an approach to simultaneously match and recover 3D people from multiple calibrated cameras. To this end, we present an affinity measure between 2D detections across different views that enforces an uncertainty geometric consistency. This similarity is then exploited by a novel multi-view matching algorithm to cluster the detections, being robust against partial observations as well as bad detections and without assuming any prior about the number of people in the scene. After that, the multi-view correspondences are used in order to efficiently infer the 3D pose of each body by means of a 3D pictorial structure model in combination with physico-geometric constraints. Our algorithm is thoroughly evaluated on challenging scenarios where several human bodies are performing different activities which involve complex motions, producing large occlusions in some views and noisy observations. We outperform state-of-the-art results in terms of matching and 3D reconstruction.
Descripció
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CitacióPérez, A.; Agudo, A. Matching and recovering 3D people from multiple views. A: IEEE Winter Conference on Applications of Computer Vision. "WACV 2022: proceedings: 2022 IEEE Winter Conference on Applications of Computer Vision: 4-8 January 2022: Waikoloa, Hawaii". Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 1184-1193. ISBN 978-1-6654-0915-5. DOI 10.1109/WACV51458.2022.00125.
ISBN978-1-6654-0915-5
Versió de l'editorhttps://ieeexplore.ieee.org/document/9706937
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