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Combining RGB and ToF cameras for real-time 3D hand gesture interaction

Published: 05 January 2011 Publication History

Abstract

Time-of-Flight (ToF) and other IR-based cameras that register depth are becoming more and more affordable in consumer electronics. This paper aims to improve a realtime hand gesture interaction system by augmenting it with a ToF camera. First, the ToF camera and the RGB camera are calibrated, and a mapping is made from the depth data to the RGB image. Then, a novel hand detection algorithm is introduced based on depth and color. This not only improves detection rates, but also allows for the hand to overlap with the face, or with hands from other persons in the background. The hand detection algorithm is evaluated in these settings, and compared to previous algorithms. Furthermore, the depth information allows us to track the position of the hand in 3D, allowing for more interesting modes of interaction. Finally, the hand gesture recognition algorithm is applied to the depth data as well, and compared to the recognition based on the RGB images. The result is a real-time hand gesture interaction system that allows for complex 3D gestures and is not disturbed by objects or persons in the background.

Cited By

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  • (2023)Binary Convolutional Neural Network for Efficient Gesture Recognition at EdgeProceedings of the Third International Conference on AI-ML Systems10.1145/3639856.3639874(1-10)Online publication date: 25-Oct-2023
  • (2022)Hand and Arm Gesture-based Human-Robot Interaction: A ReviewProceedings of the 6th International Conference on Algorithms, Computing and Systems10.1145/3564982.3564996(1-7)Online publication date: 16-Sep-2022
  • (2021)Fast 3D point-cloud segmentation for interactive surfacesCompanion Proceedings of the 2021 Conference on Interactive Surfaces and Spaces10.1145/3447932.3491141(33-37)Online publication date: 14-Nov-2021
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Published In

cover image Guide Proceedings
WACV '11: Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
January 2011
721 pages
ISBN:9781424494965

Publisher

IEEE Computer Society

United States

Publication History

Published: 05 January 2011

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Cited By

View all
  • (2023)Binary Convolutional Neural Network for Efficient Gesture Recognition at EdgeProceedings of the Third International Conference on AI-ML Systems10.1145/3639856.3639874(1-10)Online publication date: 25-Oct-2023
  • (2022)Hand and Arm Gesture-based Human-Robot Interaction: A ReviewProceedings of the 6th International Conference on Algorithms, Computing and Systems10.1145/3564982.3564996(1-7)Online publication date: 16-Sep-2022
  • (2021)Fast 3D point-cloud segmentation for interactive surfacesCompanion Proceedings of the 2021 Conference on Interactive Surfaces and Spaces10.1145/3447932.3491141(33-37)Online publication date: 14-Nov-2021
  • (2020)Plug-and-Play Gesture Control Using Muscle and Motion SensorsProceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3319502.3374823(439-448)Online publication date: 9-Mar-2020
  • (2019)On the role of multimodal learning in the recognition of sign languageMultimedia Tools and Applications10.1007/s11042-018-6565-578:8(10035-10056)Online publication date: 1-Apr-2019
  • (2018)A systematic literature review on vision based gesture recognition techniquesMultimedia Tools and Applications10.5555/3287850.328787377:21(28121-28184)Online publication date: 1-Nov-2018
  • (2018)3D finger tracking and recognition image processing for real-time music playing with depth sensorsMultimedia Tools and Applications10.1007/s11042-017-4784-977:8(9233-9248)Online publication date: 1-Apr-2018
  • (2017)GRfidIEEE Transactions on Mobile Computing10.1109/TMC.2016.254951816:2(381-393)Online publication date: 1-Feb-2017
  • (2017)Human activity recognition using segmented body part and body joint features with hidden Markov modelsMultimedia Tools and Applications10.1007/s11042-016-3742-276:11(13585-13614)Online publication date: 1-Jun-2017
  • (2016)A Deep Learning Approach to Mid-air Gesture Interaction for Mobile Devices from Time-of-Flight DataProceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/2994374.2994392(1-9)Online publication date: 28-Nov-2016
  • Show More Cited By

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