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oai:riuma.uma.es:10630/327122026-02-03T11:01:03Zcom_10630_2254col_10630_37953

Castro, Francisco M. Marín Jiménez, Manuel Jesús Guil-Mata, Nicolás Muñoz-Salinas, Rafael 2024-09-20T09:03:22Z 2024-09-20T09:03:22Z 2017 International Journal of Pattern Recognition and Artificial Intelligence, Volumen 31, Número 01, Páginas 1756002 https://hdl.handle.net/10630/32712 10.1142/S021800141756002X This paper aims to identify individuals by analyzing their gait using motion descriptors based on densely sampled short-term trajectories, instead of traditional binary silhouettes. The approach leverages advanced people detectors to create detailed spatial configurations around the person, capturing rich gait motion. Local motion features, combined using Fisher Vector encoding, result in a high-level gait descriptor called Pyramidal Fisher Motion. The method is validated on multiple datasets (CASIA, TUM GAID, CMU MoBo, and AVA Multiview Gait), achieving state-of-the-art results in recognizing individuals across various conditions such as different viewpoints, clothing, speeds, and walking paths. eng open access Arquitectura de ordenadores Fisher Motion Descriptor for Multiview Gait Recognition. journal article