ReadingAct RGB-D action dataset and human action recognition from local features

• New action dataset captured using two Kinect sensors, containing 2340 videos.
• Applied a spatio-temporal local feature approach to depth action videos.
• Developed an action recognition framework using a dynamic time alignment approach.
• Experiment on three action datasets to evaluate the advantage of depth images.
• Compared the proposed approach with traditional Bag-of-Words models.

For general home monitoring, a system should automatically interpret people’s actions. The system should be non-intrusive, and able to deal with a cluttered background, and loose clothes. An approach based on spatio-temporal local features and a Bag-of-Words (BoW) model is proposed for single-person action recognition from combined intensity and depth images. To restore the temporal structure lost in the traditional BoW method, a dynamic time alignment technique with temporal binning is applied in this work, which has not been previously implemented in the literature for human action recognition on depth imagery. A novel human action dataset with depth data has been created using two Microsoft Kinect sensors. The ReadingAct dataset contains 20 subjects and 19 actions for a total of 2340 videos. To investigate the effect of using depth images and the proposed method, testing was conducted on three depth datasets, and the proposed method was compared to traditional Bag-of-Words methods. Results showed that the proposed method improves recognition accuracy when adding depth to the conventional intensity data, and has advantages when dealing with long actions.