Authors: Richa Golash; Yogendra Kumar Jain
Addresses: Samrat Ashok Technological Institute, Vidisha, Madhya Pradesh, India ' Samrat Ashok Technological Institute, Vidisha, Madhya Pradesh, India
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.
Keywords: computer vision; deep learning; R-CNN; region-based convolutional neural network; visual object recognition; feature extraction; SIFT; scale invariant feature transform; visual object tracking.
International Journal of Computer Applications in Technology, 2021 Vol.65 No.3, pp.261 - 269
Received: 10 Apr 2020
Accepted: 18 Oct 2020
Published online: 19 Jun 2021 *