arXiv:2605.23409v1 Announce Type: cross Abstract: In human computer interaction, real-time detection and classification of dynamic hand gestures is challenging as: 1) the system must run in a real-time video stream and there is no noticeable lag in response after performing a gesture; 2) there is a large difference in how people perform gestures, making recognition more difficult. In this paper, an online hand gesture recognition system is proposed, which is able to localize gestures in real-time video stream and recognize what these gestures are. To improve the robustness of the system, the s
Advances in 3D convolutional neural networks are enabling more robust and real-time hand gesture recognition, addressing long-standing challenges in human-computer interaction.
Improved hand gesture recognition can enhance user interfaces, accelerate adoption of augmented/virtual reality, and enable more intuitive control of autonomous systems.
The ability to accurately localize and classify dynamic hand gestures in real-time video streams becomes more feasible and reliable.
- · AI/ML researchers
- · Human-computer interaction developers
- · Augmented/virtual reality companies
- · Robotics
- · Traditional input device manufacturers (potentially over time)
- · Less efficient gesture recognition methods
More natural and efficient human-machine interfaces will be developed and deployed.
This could accelerate the integration of gesture control into a wider range of consumer electronics and industrial applications.
The increased fluidity of interaction could subtly reshape human cognitive patterns around digital engagement.
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Read at arXiv cs.AI