Fusing pyramid histogram of gradients and optical flow for hand gesture recognition

In this study, we propose a multimodal feature based framework for recognising hand gestures from RGB and depth images. In addition to the features from the RGB image, the depth image features are explored into constructing the discriminative feature labels of various gestures. Depth maps having powerful source of information, increases the performance level of various computer vision problems. A newly refined Gradient-Local Binary Pattern (G-LBP) is applied to extract the features from depth images and histogram of gradients (HOG) features are extracted from RGB images. The components from both RGB and depth channels, are concatenated to form a multimodal feature vector. In the final process, classification is performed using K-Nearest Neighbour and multi-class Support Vector Machines. The designed system is invariant to scale, rotation and illumination. The newly developed feature combination method is helpful to achieve superior recognition rates for future innovations.

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Fusing pyramid histogram of gradients and optical flow for hand gesture recognition

Cited by 4 publications

References 0 publications

SpAtNet: a spatial feature attention network for hand gesture recognition

SpAtNet: a spatial feature attention network for hand gesture recognition

A novel dynamic gesture understanding algorithm fusing convolutional neural networks with hand-crafted features

Extraction of multimodal features from depth and RGB images for recognising hand gestures

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