Structure-Aware 3D Hand Pose Regression from a Single Depth Image

Malik, Jameel; Elhayek, Ahmed; Stricker, Didier

doi:10.1007/978-3-030-01790-3_1

Cited by 27 publications

(16 citation statements)

References 31 publications

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“…In (Tekin et.al., 2019), a model for recognizing a hand action using a single RGB image was proposed. In (Malik et. al., 2018), a new algorithm based on a 3D CNN was proposed, which learns to detect a hand from a 3D image.…”

Section: Related Workmentioning

confidence: 99%

Method of Multi-Modal Video Analysis of Hand Movements for Automatic Recognition of Isolated Signs of Russian Sign Language

Axyonov

Ryumin

Kagirov

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This paper presents a new method for collecting multimodal sign language (SL) databases, which is distinguished by the use of multimodal video data. The paper also proposes a new method of multimodal sign recognition, which is distinguished by the analysis of spatio-temporal visual features of SL units (i.e. lexemes). Generally, gesture recognition is a processing of a video sequence, which helps to extract information on movements of any articulator (a part of the human body) in time and space. With this approach, the recognition accuracy of isolated signs was 88.92%. The proposed method, due to the extraction and analysis of spatio-temporal data, makes it possible to identify more informative features of signs, which leads to an increase in the accuracy of SL recognition.

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Section: Related Workmentioning

confidence: 99%

Method of Multi-Modal Video Analysis of Hand Movements for Automatic Recognition of Isolated Signs of Russian Sign Language

Axyonov

Ryumin

Kagirov

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Initially, a CNN was employed to predict and bone scale and pose parameters, and, subsequently, a forward kinematics layer processed them to infer the 3D joint positions. The same authors [91] further imposed structural constraints, considering length and inter-distance of fingers, as well as kinematic distance, to better capture the hand shape. They regressed the center of the hand using a CNN, and the cropped hand region was then fed to another CNN, namely PoseCNN, to estimate the hand pose coupled with the above constraints.…”

Section: D Depth Map Utilizationmentioning

confidence: 99%

A Comprehensive Study on Deep Learning-Based 3D Hand Pose Estimation Methods

et al. 2020

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The field of 3D hand pose estimation has been gaining a lot of attention recently, due to its significance in several applications that require human-computer interaction (HCI). The utilization of technological advances, such as cost-efficient depth cameras coupled with the explosive progress of Deep Neural Networks (DNNs), has led to a significant boost in the development of robust markerless 3D hand pose estimation methods. Nonetheless, finger occlusions and rapid motions still pose significant challenges to the accuracy of such methods. In this survey, we provide a comprehensive study of the most representative deep learning-based methods in literature and propose a new taxonomy heavily based on the input data modality, being RGB, depth, or multimodal information. Finally, we demonstrate results on the most popular RGB and depth-based datasets and discuss potential research directions in this rapidly growing field.

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“…The model proposed in [TCT*16] improves robustness of hands tracking as well as surface alignment for users via minimizing a cost function termed golden energy. Recently, learning‐based methods are also developed in hand modeling from depth maps [MDB*19, ZXCZ20, MES18, CML18, YGS*18, HHY*19]. Mueller et al [MDB*19] succeed in tracking two touching hands with only one depth camera by embedding a neural network into the energy minimization framework.…”

Section: Related Workmentioning

confidence: 99%