Depth Based Static Hand Gesture Segmentation and Recognition

Kumar, N. C. Dayananda; Suresh, K. V.; Dinesh, R.

doi:10.1007/978-3-031-22405-8_11

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Coarse to fine segmentation approach using depth map was proposed in [8] where pre-trained YOLO-v3 model was used to detect and localize the hand region at coarse level. The hand detected bounding region was used to initialize the foreground in graph cut segmentation algorithm which refines the hand region boundary and discards the background.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Depth modality can be effectively used to discard the far away background based on depth distance range. It helps in effective hand segmentation to group hand region pixels into foreground and rest of the image pixels into background [8]. Also in case of low light scenarios RGB sensors fails to capture the data, this issue can be resolved using Kinect depth sensor as it captures the data using IR light.…”

Section: Introductionmentioning

confidence: 99%

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SSEC: Semantic Segmentation and Ensemble Classification Framework for Static Hand Gesture Recognition using RGB-D Data

NC¹,

Suresh²,

Chandrasekhar³

et al. 2023

IJACSA

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Hand Gesture Recognition (HGR) refers to identifying various hand postures used in Sign Language Recognition (SLR) and Human Computer Interaction (HCI) applications. Complex background in uncontrolled environmental condition is the major challenging issue which impacts the recognition accuracy of HGR system. This can be effectively addressed by discarding the background using suitable semantic segmentation method, where it predicts the hand region pixels into foreground and rest of the pixels into background. In this paper, we have analyzed and evaluated well known semantic segmentation architectures for hand region segmentation using both RGB and depth data. Further, ensemble of segmented RGB and depth stream is used for hand gesture classification through probability score fusion. Experimental results shows that the proposed novel framework of Semantic Segmentation and Ensemble Classification (SSEC) is suitable for static hand gesture recognition and achieved F1-score of 88.91% on OUHANDS test dataset.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SSEC: Semantic Segmentation and Ensemble Classification Framework for Static Hand Gesture Recognition using RGB-D Data

NC¹,

Suresh²,

Chandrasekhar³

et al. 2023

IJACSA

View full text Add to dashboard Cite

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Blsnet: a tri-branch lightweight network for gesture segmentation against cluttered backgrounds

Zhou,

Cui,

2023

Complex Intell. Syst.

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Hand gesture segmentation is an essential step to recognize hand gestures for human–robot interaction. However, complex backgrounds and the variety of gesture shapes cause low semantic segmentation accuracy in the existing lightweight methods because of imprecise features and imbalance between branches. To remedy the above problems, we propose a new segmentation structure for hand gestures. Based on the structure, a novel tri-branch lightweight segmentation network (BLSNet), is proposed for gesture segmentation. Corresponding to the structure parts, three branches are employed to achieve local features, boundaries and semantic hand features. In the boundary branch, to extract multiscale features of hand gesture contours, a novel multi-scale depth-wise strip convolution (MDSC) module is proposed based on gesture boundaries for directionality. For hand boundary details, we propose a new boundary weight (BW) module based on boundary attention. To identify hand location, a semantic branch with continuous downsampling is used to address complex backgrounds. We use the Ghost bottleneck as the building block for the entire BLSNet network. To verify the effectiveness of the proposed network, corresponding experiments have been conducted based on OUHANDS and HGR1 datasets, and the experimental results demonstrate that the proposed method is superior to contrast methods.

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FGDSNet: A Lightweight Hand Gesture Recognition Network for Human Robot Interaction

Zhou,

Cui,

2024

IEEE Robot. Autom. Lett.

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Depth Based Static Hand Gesture Segmentation and Recognition

Cited by 3 publications

References 18 publications

SSEC: Semantic Segmentation and Ensemble Classification Framework for Static Hand Gesture Recognition using RGB-D Data

SSEC: Semantic Segmentation and Ensemble Classification Framework for Static Hand Gesture Recognition using RGB-D Data

Blsnet: a tri-branch lightweight network for gesture segmentation against cluttered backgrounds

FGDSNet: A Lightweight Hand Gesture Recognition Network for Human Robot Interaction

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