Extraction Of Cumulative Blobs From Dynamic Gestures

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 environment. So, the video stream from the camera is fed into a Raspberry Pi which has a Python program running OpenCV module which is used for detecting, isolating and tracking the path of dynamic gesture, then we use an algorithm of machine learning to recognize the pattern drawn and accordingly control the GPIOs of the raspberry pi to perform some activities.