Novel spatio-temporal features for fingertip writing recognition in egocentric viewpoint
Muhammad Zaid Hameed, Guillermo Garcia-Hernando · 2015
In this paper, we propose a novel feature extraction scheme for fingertip writing recognition in the air for egocentric viewpoint. The inherent challenges in the egocentric vision e.g. rapid camera motion and object's appearance and disappearance in scene may cause the fingertip to be detected in non-uniformly time separated frames. Most existing approaches do not consider this missing temporal information for feature extraction, which could be utilized to improve performance in ego-vision tasks. The novel feature extraction scheme extracts spatio-temporal features from trajectory of hand movement which are used with Hidden Markov Models for classification. The proposed feature set outperforms current trajectory based feature schemes and achieves 96.7% recognition rate on a novel fingertip trajectory dataset.