Study and Survey on Gesture Recognition Systems

• URL: http://arxiv.org/abs/2312.00392v1
• Date: Fri, 1 Dec 2023 07:29:30 GMT
• Title: Study and Survey on Gesture Recognition Systems
• Authors: Kshitij Deshpande, Varad Mashalkar, Kaustubh Mhaisekar, Amaan Naikwadi and Archana Ghotkar
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, there has been a considerable amount of research in the Gesture Recognition domain, mainly owing to the technological advancements in Computer Vision. Various new applications have been conceptualised and developed in this field. This paper discusses the implementation of gesture recognition systems in multiple sectors such as gaming, healthcare, home appliances, industrial robots, and virtual reality. Different methodologies for capturing gestures are compared and contrasted throughout this survey. Various data sources and data acquisition techniques have been discussed. 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.

Related papers

• 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)
• An inclusive review on deep learning techniques and their scope in handwriting recognition [4.318047857743103]
  Deep learning expresses a category of machine learning algorithms that have the capability to combine raw inputs into intermediate features layers. Deep learning has particularly witnessed for a great achievement of human level performance across a number of domains in computer vision and pattern recognition. This paper presents a survey on the existing studies of deep learning in handwriting recognition field.
  arXiv  Detail & Related papers  (2024-04-10T06:30:33Z)
• Bridging Machine Learning and Sciences: Opportunities and Challenges [0.0]
  Application of machine learning in sciences has seen exciting advances in recent years. Recently, deep neural nets-based out-of-distribution detection has made great progress for high-dimensional data. We take a critical look at their applicative prospects including data universality, experimental protocols, model robustness, etc.
  arXiv  Detail & Related papers  (2022-10-24T17:54:46Z)
• SHREC 2021: Track on Skeleton-based Hand Gesture Recognition in the Wild [62.450907796261646]
  Recognition of hand gestures can be performed directly from the stream of hand skeletons estimated by software. Despite the recent advancements in gesture and action recognition from skeletons, it is unclear how well the current state-of-the-art techniques can perform in a real-world scenario. This paper presents the results of the SHREC 2021: Track on Skeleton-based Hand Gesture Recognition in the Wild contest.
  arXiv  Detail & Related papers  (2021-06-21T10:57:49Z)
• Weakly Supervised Object Localization and Detection: A Survey [145.5041117184952]
  weakly supervised object localization and detection plays an important role for developing new generation computer vision systems. We review (1) classic models, (2) approaches with feature representations from off-the-shelf deep networks, (3) approaches solely based on deep learning, and (4) publicly available datasets and standard evaluation metrics that are widely used in this field. We discuss the key challenges in this field, development history of this field, advantages/disadvantages of the methods in each category, relationships between methods in different categories, applications of the weakly supervised object localization and detection methods, and potential future directions to further promote the development of this research field
  arXiv  Detail & Related papers  (2021-04-16T06:44:50Z)
• 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)
• Recent Progress in Appearance-based Action Recognition [73.6405863243707]
  Action recognition is a task to identify various human actions in a video. Recent appearance-based methods have achieved promising progress towards accurate action recognition.
  arXiv  Detail & Related papers  (2020-11-25T10:18:12Z)
• 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)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)
• 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.