Real-time automatic tracking of hand motion in RGB videos using local feature SIFT
by Richa Golash; Yogendra Kumar Jain
International Journal of Intelligent Systems Design and Computing (IJISDC), Vol. 3, No. 2, 2020

Abstract: This paper proposes a method for real-time visual tracking of moving hand in RGB videos without any segmentation process and background subtraction. We have used YC_gC_r converted version of YC_bC_r colour space for a more compact representation of the initial region of moving hand and then local feature SIFT to detect and track hand simultaneously. YC_gC_r has a high tendency for skin colour accretion and can effectively discriminate between the skin and non-skin colour regions. The approach demonstrates that using local features (SIFT) of only active region reduces the computation as well as make the method free from the challenges of freedom factor of hand and thus the methodology can detect the hand of any shape and size without being affected by background conditions. In general, researchers avoid using a normal camera for applications based on hand tracking, as RGB images are sensitive to illumination. Our work exhibits that the combination of YC_gC_r and two-stage feature matching through SIFT algorithm is successful in tracking non-rigid objects with less computation. The methodology is further evaluated with Kalman tracking in hand gesture recognition and is also compared with contemporary works.

Online publication date: Fri, 21-May-2021

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