Robust multi-dimensional motion features for first-person vision activity recognition
Visualitza/Obre
Cita com:
hdl:2117/88837
Tipus de documentArticle
Data publicació2016-08
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement 3.0 Espanya
Abstract
We propose robust multi-dimensional motion features for human activity recognition from first-person videos. The proposed features encode information about motion magnitude, direction and variation, and combine them with virtual inertial data generated from the video itself. The use of grid flow representation, per-frame normalization and temporal feature accumulation enhances the robustness of our new representation. Results on multiple datasets demonstrate that the proposed feature representation outperforms existing motion features, and importantly it does so independently of the classifier. Moreover, the proposed multi-dimensional motion features are general enough to make them suitable for vision tasks beyond those related to wearable cameras. (C) 2015 The Authors. Published by Elsevier Inc.
CitacióAbebe, G., Cavallaro, A., Llanas, F. Robust multi-dimensional motion features for first-person vision activity recognition. "Computer vision and image understanding", Agost 2016, vol. 149, p. 229-248.
ISSN1077-3142
Versió de l'editorhttp://www.sciencedirect.com/science/article/pii/S1077314215002350
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
CVIU_229.pdf | 3,725Mb | Visualitza/Obre |