3D Visual Detection of Correct NGT Sign Production

3D Visual Detection of Correct NGT Sign Production

Lichtenauer et al using 3D visual tracking to classify Dutch sign language. Using a single camera they can capture 2D features: x and y position, displacement, motion angle, and velocities. With 2 cameras this can be converted into 3D positions, angles, and velocities. DTW is used to map gestures to a reference gesture. Gestures are mapped on an individual feature basis creating classifiers for each feature. The average classification is determined as the output classification. The feature classifiers determine if an example feature falls within the range of 90% of a Gaussian modeling the positive examples of the class, and a sign is assigned to classifications if the average of the features is above a threshold.

Discussion
This paper is interesting in that it is the first one using DTW. While the classification seems fairly accurate, they don't appear to decide between classes only if an example belongs to a given class.


Reference
Title: 3D Visual Detection of Correct NGT Sign Production Authors: J.F. Lichtenauer, G.A. ten Holt, E.A. Hendriks, M.J.T. Reinders