Extraction Of Cumulative Blobs From Dynamic Gestures
Rishabh Naulakha, Shubham Gaur, Dhairya Lodha, Mehek Tulsyan, Utsav, Kotecha

TL;DR
This paper presents a gesture recognition system using infrared night vision cameras and OpenCV on Raspberry Pi, enabling gesture-based control in dark environments by extracting cumulative blobs from dynamic gestures.
Contribution
It introduces a novel approach combining infrared night vision with blob extraction and machine learning for robust gesture recognition in low-light conditions.
Findings
Effective gesture detection in dark environments
High accuracy in dynamic gesture recognition
Real-time control of Raspberry Pi GPIOs
Abstract
Gesture recognition is a perceptual user interface, which is based on CV technology that allows the computer to interpret human motions as commands, allowing users to communicate with a computer without the use of hands, thus making the mouse and keyboard superfluous. Gesture recognition's main weakness is a light condition because gesture control is based on computer vision, which heavily relies on cameras. These cameras are used to interpret gestures in 2D and 3D, so the extracted information can vary depending on the source of light. The limitation of the system cannot work in a dark environment. A simple night vision camera can be used as our camera for motion capture as they also blast out infrared light which is not visible to humans but can be clearly seen with a camera that has no infrared filter this majorly overcomes the limitation of systems which cannot work in a dark…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsInteractive and Immersive Displays
