Braun, Andreas and Alekseew, Michael and Kuijper, Arjan (2016):
Exploring Machine Learning Object Classification for Interactive Proximity Surfaces.
In: Lecture Notes in Computer Science (LNCS); 9749, In: Distributed, Ambient, and Pervasive Interactions, pp. 157-167,
Springer International Publishing, Switzerland, 4th International Conference, DAPI 2016, Toronto, ON, Canada, July 17-22, 2016, DOI: 10.1007/978-3-319-39862-4₁₅,
[Conference or Workshop Item]
Abstract
Capacitive proximity sensors are a variety of the sensing technology that drives most finger-controlled touch screens today. However, they work over a larger distance. As they are not disturbed by non-conductive materials, they can be used to track hands above arbitrary surfaces, creating flexible interactive surfaces. Since the resolution is lower compared to many other sensing technologies, it is necessary to use sophisticated data processing methods for object recognition and tracking. In this work we explore machine learning methods for the detection and tracking of hands above an interactive surface created with capacitive proximity sensors. We discuss suitable methods and present our implementation based on Random Decision Forests. The system has been evaluated on a prototype interactive surface - the CapTap. Using a Kinect-based hand tracking system, we collect training data and compare the results of the learning algorithm to actual data.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2016 |
| Creators: | Braun, Andreas and Alekseew, Michael and Kuijper, Arjan |
| Title: | Exploring Machine Learning Object Classification for Interactive Proximity Surfaces |
| Language: | English |
| Abstract: | Capacitive proximity sensors are a variety of the sensing technology that drives most finger-controlled touch screens today. However, they work over a larger distance. As they are not disturbed by non-conductive materials, they can be used to track hands above arbitrary surfaces, creating flexible interactive surfaces. Since the resolution is lower compared to many other sensing technologies, it is necessary to use sophisticated data processing methods for object recognition and tracking. In this work we explore machine learning methods for the detection and tracking of hands above an interactive surface created with capacitive proximity sensors. We discuss suitable methods and present our implementation based on Random Decision Forests. The system has been evaluated on a prototype interactive surface - the CapTap. Using a Kinect-based hand tracking system, we collect training data and compare the results of the learning algorithm to actual data. |
| Title of Book: | Distributed, Ambient, and Pervasive Interactions |
| Series Name: | Lecture Notes in Computer Science (LNCS); 9749 |
| Publisher: | Springer International Publishing, Switzerland |
| Uncontrolled Keywords: | Guiding Theme: Smart City, Research Area: Human computer interaction (HCI), Interactive surfaces, Machine learning, Capacitive sensors, Proximity sensing |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Interactive Graphics Systems 20 Department of Computer Science > Mathematical and Applied Visual Computing |
| Event Title: | 4th International Conference, DAPI 2016 |
| Event Location: | Toronto, ON, Canada |
| Event Dates: | July 17-22, 2016 |
| Date Deposited: | 06 May 2019 07:22 |
| DOI: | 10.1007/978-3-319-39862-4₁₅ |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Send an inquiry |
Options (only for editors)
 |
Show editorial Details |
Drucken
Impressum