Human Action/Gesture recognition is a challenging area of research that deals with the problem of recognizing people in images, detecting and describing body parts, inferring their spatial configuration, and performing action/gesture recognition from still images or image sequences. This process may also include multi-modal representations, including signals from wearable devices as well as multi-modal visual representations, such as RGB and Depth data.
Because of the huge space of human configurations, body pose recovery is a difficult problem that involves dealing with several distortions: illumination changes, partial occlusions, changes in the point of view, rigid and elastic deformations, or high inter and intra-class variability, just to mention a few. Even with the high difficulty of the problem, modern Computer Vision techniques and new tendencies deserve further attention, and promising results are expected in the next years. Moreover, several subareas have been recently defined, such as Affective Computing, Social Signal Processing, Human Behavior Analysis and Social Robotics. The effort involved in this area of research will be compensated by its potential applications: TV production, home entertainment (multimedia content analysis), education purposes, sociology research, surveillance and security, improved quality of life by means of monitoring or automatic artificial assistance, etc.
The main research interests of the group are:
- Human body pose recovery and face analysis in RGB and multi-modal data representations
- Human Behavior Analysis
- Multi-class classification and learning systems
- Smart environments and Human-Computer Interaction
- eHealth intelligent systems