Comparing grid-based features and classification settings, in an off-line supervised human action detection and recognition task

This master thesis describes a final project, where two different grid-based features using different classification settings are compared, in an off-line supervised human action detection and recognition task. The task consists of the detecting and recognizing fitness moves performed in an exercise video, using limited training data acquired from the same video.