Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10553/54978
Título: | Ego-motion classification for body-worn videos | Autores/as: | Meng, Zhaoyi Sánchez, Javier Morel, Jean Michel Bertozzi, Andrea L. Brantingham, P. Jeffrey |
Clasificación UNESCO: | 220990 Tratamiento digital. Imágenes | Fecha de publicación: | 2018 | Publicación seriada: | Mathematics and Visualization | Conferencia: | International conference on Imaging, Vision and Learning Based on Optimization and PDEs, IVLOPDE 2016 | Resumen: | Portable cameras record dynamic first-person video footage and these videos contain information on the motion of the individual to whom the camera is mounted, defined as ego. We address the task of discovering ego-motion from the video itself, without other external calibration information. We investigate the use of similarity transformations between successive video frames to extract signals reflecting ego-motions and their frequencies. We use novel graph-based unsupervised and semi-supervised learning algorithms to segment the video frames into different ego-motion categories. Our results show very accurate results on both choreographed test videos and ego-motion videos provided by the Los Angeles Police Department. | URI: | http://hdl.handle.net/10553/54978 | ISSN: | 1612-3786 | DOI: | 10.1007/978-3-319-91274-5_10 | Fuente: | Tai XC., Bae E., Lysaker M. (eds) Imaging, Vision and Learning Based on Optimization and PDEs. IVLOPDE 2016. Mathematics and Visualization. Springer, Cham |
Colección: | Actas de congresos |
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