Extraction Of Cumulative Blobs From Dynamic Gestures

2025-01-07

AI Summary

Traditional gesture recognition systems suffer severe performance degradation and fail to support human–computer interaction under low-light conditions. To address this, this paper proposes a dynamic gesture recognition method integrating infrared night-vision imaging with cumulative Blob-based spatiotemporal feature extraction. An infrared camera captures visible-light-independent video streams; background modeling, contour detection, and trajectory tracking are implemented on a Raspberry Pi embedded platform using OpenCV, followed by extraction of cumulative Blob features encoding both spatial shape and temporal motion patterns. Six dynamic gestures are classified using SVM or Random Forest classifiers. This work is the first to combine infrared imaging with cumulative Blob analysis for robust low-illumination gesture tracking, overcoming a critical applicability bottleneck of vision-based gesture systems in dark environments. Experimental results demonstrate an average recognition accuracy of 92.3%, end-to-end latency under 120 ms, and real-time control of GPIO peripherals (e.g., LEDs, buzzers), achieving all-weather operation, low power consumption, and high robustness.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Gesture Recognition
• Low Light Conditions
• Human-Computer Interaction

Innovation

• Night Vision Camera
• Raspberry Pi
• Gesture Recognition in Low Light