Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/54978
Title: Ego-motion classification for body-worn videos
Authors: Meng, Zhaoyi
Sánchez, Javier 
Morel, Jean Michel
Bertozzi, Andrea L.
Brantingham, P. Jeffrey
UNESCO Clasification: 220990 Tratamiento digital. Imágenes
Issue Date: 2018
Publisher: 1612-3786
Journal: Mathematics and Visualization 
Abstract: 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
Source: Tai XC., Bae E., Lysaker M. (eds) Imaging, Vision and Learning Based on Optimization and PDEs. IVLOPDE 2016. Mathematics and Visualization. Springer, Cham
Appears in Collections:Actas de Congresos

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