Robust tracking of moving hand in coloured video acquired through simple camera
by Richa Golash; Yogendra Kumar Jain
International Journal of Computer Applications in Technology (IJCAT), Vol. 65, No. 3, 2021

Abstract: Interaction through dynamic hand gestures is an interesting yet challenging area because in computer vision the hand is a non-rigid object that moves unpredictably. Additionally, the real-time backgrounds are unstable. RGB data of moving hand are sensitive to light variation and camera-view thus, continuous localisation of hand region in RGB images is strenuous. This paper proposes a unique solution that combines Scale-Invariant Feature Transform (SIFT) features with automatic feature extraction mechanism of Region-based Convolutional Neural Network (R-CNN), for robust tracking of moving hand in coloured video acquired through a camera with normal resolution. The efficiency of the proposed methodology is 96.84% in simple and 94.73% in complex background. The comparative analysis with contemporary techniques working on RGB images exhibits that the proposed solution gives high accuracy in a real-time environment. In the future, we can design an economical and user-friendly natural user interface using the proposed technique.

Online publication date: Tue, 06-Jul-2021

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