2026-04-17T17:31:28Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/161712026-02-01T01:46:09Zcom_2072_1033col_2072_452950

Approximate partitioning of observations in hierarchical particle filter body tracking López Méndez, Adolfo Alcoverro Vidal, Marcel Pardàs Feliu, Montse Casas Pla, Josep Ramon Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació::Interacció home-màquina Human locomotion Mecànica humana This paper presents a model-based hierarchical particle filtering algorithm to estimate the pose and anthropometric parameters of humans in multi-view environments. Our method incorporates a novel likelihood measurement approach consisting of an approximate partitioning of observations. Provided that a partitioning of the human body model has been defined and associates body parts to state space variables, the proposed method estimates image regions that are relevant to that body part and thus to the state space variables of interest. The proposed regions are bounding boxes and consequently can be efficiently processed in a GPU. The algorithm is tested in a challenging dataset involving people playing tennis (TennisSense) and also in the well-known HumanEva dataset. The obtained results show the effectiveness of the proposed method. Peer Reviewed Postprint (published version) 2011 Conference report López-Mendez, A. [et al.]. Approximate partitioning of observations in hierarchical particle filter body tracking. A: IEEE Conference on Computer Vision and Pattern Recognition. "2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops". 2011, p. 19-24. 978-1-4577-0529-8 https://hdl.handle.net/2117/16171 10.1109/CVPRW.2011.5981712 eng http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981712 Restricted access - publisher's policy 6 p. application/pdf