Survey on Hand Gesture Recognition from Visual Input
- URL: http://arxiv.org/abs/2501.11992v1
- Date: Tue, 21 Jan 2025 09:23:22 GMT
- Title: Survey on Hand Gesture Recognition from Visual Input
- Authors: Manousos Linardakis, Iraklis Varlamis, Georgios Th. Papadopoulos,
- Abstract summary: Hand gesture recognition has become an important research area, driven by the growing demand for human-computer interaction.
There are few surveys that comprehensively cover recent research developments, available solutions, and benchmark datasets.
This survey addresses this gap by examining the latest advancements in hand gesture and 3D hand pose recognition from various types of camera input data.
- Score: 2.1591725778863555
- License:
- Abstract: Hand gesture recognition has become an important research area, driven by the growing demand for human-computer interaction in fields such as sign language recognition, virtual and augmented reality, and robotics. Despite the rapid growth of the field, there are few surveys that comprehensively cover recent research developments, available solutions, and benchmark datasets. This survey addresses this gap by examining the latest advancements in hand gesture and 3D hand pose recognition from various types of camera input data including RGB images, depth images, and videos from monocular or multiview cameras, examining the differing methodological requirements of each approach. Furthermore, an overview of widely used datasets is provided, detailing their main characteristics and application domains. Finally, open challenges such as achieving robust recognition in real-world environments, handling occlusions, ensuring generalization across diverse users, and addressing computational efficiency for real-time applications are highlighted to guide future research directions. By synthesizing the objectives, methodologies, and applications of recent studies, this survey offers valuable insights into current trends, challenges, and opportunities for future research in human hand gesture recognition.
Related papers
- A Survey of Stance Detection on Social Media: New Directions and Perspectives [50.27382951812502]
stance detection has emerged as a crucial subfield within affective computing.
Recent years have seen a surge of research interest in developing effective stance detection methods.
This paper provides a comprehensive survey of stance detection techniques on social media.
arXiv Detail & Related papers (2024-09-24T03:06:25Z) - A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities [1.6144710323800757]
Hand Gesture Recognition (HGR) systems enhance natural, efficient, and authentic human-computer interaction.
Despite significant progress, automatic and precise identification of hand gestures remains a considerable challenge in computer vision.
This paper provides a comprehensive review of HGR techniques and data modalities from 2014 to 2024, exploring advancements in sensor technology and computer vision.
arXiv Detail & Related papers (2024-08-10T04:40:01Z) - Survey on Emotion Recognition through Posture Detection and the possibility of its application in Virtual Reality [0.0]
A survey is presented focused on using pose estimation techniques in Emotional recognition using various technologies normal cameras, and depth cameras for real-time, and the potential use of VR and inputs including images, videos, and 3-dimensional poses described in vector space.
We discussed 19 research papers collected from selected journals and databases highlighting their methodology, classification algorithm, and the used datasets that relate to emotion recognition and pose estimation.
arXiv Detail & Related papers (2024-08-03T10:01:29Z) - Deep Learning-Based Object Pose Estimation: A Comprehensive Survey [73.74933379151419]
We discuss the recent advances in deep learning-based object pose estimation.
Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks.
arXiv Detail & Related papers (2024-05-13T14:44:22Z) - Deepfake Generation and Detection: A Benchmark and Survey [134.19054491600832]
Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions.
This survey comprehensively reviews the latest developments in deepfake generation and detection.
We focus on researching four representative deepfake fields: face swapping, face reenactment, talking face generation, and facial attribute editing.
arXiv Detail & Related papers (2024-03-26T17:12:34Z) - Data Augmentation in Human-Centric Vision [54.97327269866757]
This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks.
It delves into a wide range of research areas including person ReID, human parsing, human pose estimation, and pedestrian detection.
Our work categorizes data augmentation methods into two main types: data generation and data perturbation.
arXiv Detail & Related papers (2024-03-13T16:05:18Z) - Study and Survey on Gesture Recognition Systems [0.0]
This paper discusses the implementation of gesture recognition systems in multiple sectors such as gaming, healthcare, home appliances, industrial robots, and virtual reality.
The role of gestures in sign language has been studied and existing approaches have been reviewed.
Common challenges faced while building gesture recognition systems have also been explored.
arXiv Detail & Related papers (2023-12-01T07:29:30Z) - General Place Recognition Survey: Towards the Real-world Autonomy Age [36.49196034588173]
The place recognition community has made astonishing progress over the last $20$ years.
Few methods have shown promising place recognition performance in complex real-world scenarios.
This paper can be a tutorial for researchers new to the place recognition community and those who care about long-term robotics autonomy.
arXiv Detail & Related papers (2022-09-09T19:37:05Z) - Deep Gait Recognition: A Survey [15.47582611826366]
Gait recognition is an appealing biometric modality which aims to identify individuals based on the way they walk.
Deep learning has reshaped the research landscape in this area since 2015 through the ability to automatically learn discriminative representations.
We present a comprehensive overview of breakthroughs and recent developments in gait recognition with deep learning.
arXiv Detail & Related papers (2021-02-18T18:49:28Z) - Survey on the Analysis and Modeling of Visual Kinship: A Decade in the
Making [66.72253432908693]
Kinship recognition is a challenging problem with many practical applications.
We review the public resources and data challenges that enabled and inspired many to hone-in on the views.
For the tenth anniversary, the demo code is provided for the various kin-based tasks.
arXiv Detail & Related papers (2020-06-29T13:25:45Z) - Deep Learning for Sensor-based Human Activity Recognition: Overview,
Challenges and Opportunities [52.59080024266596]
We present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition.
We first introduce the multi-modality of the sensory data and provide information for public datasets.
We then propose a new taxonomy to structure the deep methods by challenges.
arXiv Detail & Related papers (2020-01-21T09:55:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.