Relative dense tracklets for human action recognition

This paper addresses the problem of recognizing human actions in video sequences for home care applications. Recent studies have shown that approaches which use a bag-of-words representation reach high action recognition accuracy. Unfortunately, these approaches have problems to discriminate similar actions, ignoring spatial information of features. As we focus on recognizing subtle differences in behaviour of patients, we propose a novel method which significantly enhances the discriminative properties of the bag-of-words technique. Our approach is based on a dynamic coordinate system, which introduces spatial information to the bag-of-words model, by computing relative tracklets. We perform an extensive evaluation of our approach on three datasets: popular KTH dataset, challenging ADL dataset and our collected Hospital dataset. Experiments show that our representation enhances the discriminative power of features and bag-of-words model, bringing significant improvements in action recognition performance.