Journal
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
Volume 53, Issue 1, Pages 35-43Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/THMS.2022.3188840
Keywords
Sensors; Hidden Markov models; Monitoring; Tracking; Gesture recognition; Training; Spatial resolution; Contactless systems; gesture recognition; hand tracking; motion capture; multimodal
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This article provides an overview of contemporary methods used for hand tracking and gesture recognition, including the use of contactless devices and multiple sensors to overcome limitations of monocular vision systems. It also introduces common steps, techniques, and algorithms used in developing these systems and predicts future trends.
Hand tracking and gesture recognition are fundamental in a multitude of applications. Various sensors have been used for this purpose, however, all monocular vision systems face limitations caused by occlusions. Wearable equipment overcome said limitations, although deemed impractical in some cases. Using more than one sensor provides a way to overcome this problem, but necessitates more complicated designs. In this work, we aim to highlight contemporary methods used for hand tracking and gesture recognition by collecting publications of systems developed in the last decade, that employ contactless devices as RGB cameras, IR, and depth sensors, along with some preceding pillar works. Additionally, we briefly present common steps, techniques, and basic algorithms used during the process of developing modern hand tracking and gesture recognition systems and, finally, we derive the trend for the next future.
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