Interpreting Hand gestures using Object Detection and Digits Classification

• URL: http://arxiv.org/abs/2407.10902v1
• Date: Mon, 15 Jul 2024 16:53:04 GMT
• Title: Interpreting Hand gestures using Object Detection and Digits Classification
• Authors: Sangeetha K, Balaji VS, Kamalesh P, Anirudh Ganapathy PS,
• Abstract summary: This research aims to develop a robust system that can accurately recognize and classify hand gestures representing numbers. The proposed approach involves collecting a dataset of hand gesture images, preprocessing and enhancing the images, extracting relevant features, and training a machine learning model.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Hand gestures have evolved into a natural and intuitive means of engaging with technology. The objective of this research is to develop a robust system that can accurately recognize and classify hand gestures representing numbers. The proposed approach involves collecting a dataset of hand gesture images, preprocessing and enhancing the images, extracting relevant features, and training a machine learning model. The advancement of computer vision technology and object detection techniques, in conjunction with OpenCV's capability to analyze and comprehend hand gestures, presents a chance to transform the identification of numerical digits and its potential applications. The advancement of computer vision technology and object identification technologies, along with OpenCV's capacity to analyze and interpret hand gestures, has the potential to revolutionize human interaction, boosting people's access to information, education, and employment opportunities. Keywords: Computer Vision, Machine learning, Deep Learning, Neural Networks

Related papers

• Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms [5.734290974917728]
  This paper focuses on the analysis of the application effectiveness of the integration of deep learning and computer vision technologies. Deep learning achieves a historic breakthrough by constructing hierarchical neural networks, enabling end-to-end feature learning and semantic understanding of images. The successful experiences in the field of computer vision provide strong support for training deep learning algorithms.
  arXiv  Detail & Related papers  (2023-12-20T09:37:06Z)
• Human-oriented Representation Learning for Robotic Manipulation [64.59499047836637]
  Humans inherently possess generalizable visual representations that empower them to efficiently explore and interact with the environments in manipulation tasks. We formalize this idea through the lens of human-oriented multi-task fine-tuning on top of pre-trained visual encoders. Our Task Fusion Decoder consistently improves the representation of three state-of-the-art visual encoders for downstream manipulation policy-learning.
  arXiv  Detail & Related papers  (2023-10-04T17:59:38Z)
• Exploring Human Crowd Patterns and Categorization in Video Footage for Enhanced Security and Surveillance using Computer Vision and Machine Learning [0.0]
  This paper explores computer vision's potential in security and surveillance, presenting a novel approach to track motion in videos. By categorizing motion into Arcs, Lanes, Converging/Diverging, and Random/Block motions, the paper examines different optical flow techniques, CNN models, and machine learning models. The results can train anomaly-detection models, provide behavioral insights based on motion, and enhance scene comprehension.
  arXiv  Detail & Related papers  (2023-08-26T16:09:20Z)
• RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot [56.130215236125224]
  A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots. Recent research in one-shot imitation learning has shown promise in transferring trained policies to new tasks based on demonstrations. This paper aims to unlock the potential for an agent to generalize to hundreds of real-world skills with multi-modal perception.
  arXiv  Detail & Related papers  (2023-07-02T15:33:31Z)
• Transfer: Cross Modality Knowledge Transfer using Adversarial Networks -- A Study on Gesture Recognition [7.742297876120562]
  We propose a generic framework for knowledge transfer between a source and a target technology. TRANSFER uses a language-based representation of a hand gesture, which captures a temporal combination of concepts. We demonstrate the usage of TRANSFER for three different scenarios.
  arXiv  Detail & Related papers  (2023-06-26T23:47:59Z)
• Agile gesture recognition for capacitive sensing devices: adapting on-the-job [55.40855017016652]
  We demonstrate a hand gesture recognition system that uses signals from capacitive sensors embedded into the etee hand controller. The controller generates real-time signals from each of the wearer five fingers. We use a machine learning technique to analyse the time series signals and identify three features that can represent 5 fingers within 500 ms.
  arXiv  Detail & Related papers  (2023-05-12T17:24:02Z)
• HANDS: A Multimodal Dataset for Modeling Towards Human Grasp Intent Inference in Prosthetic Hands [3.7886097009023376]
  Advanced prosthetic hands of the future are anticipated to benefit from improved shared control between a robotic hand and its human user. multimodal sensor data may include various environment sensors including vision, as well as human physiology and behavior sensors. A fusion methodology for environmental state and human intent estimation can combine these sources of evidence in order to help prosthetic hand motion planning and control.
  arXiv  Detail & Related papers  (2021-03-08T15:51:03Z)
• Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery [84.73764603474413]
  We propose a novel online approach of multi-modal graph network (i.e., MRG-Net) to dynamically integrate visual and kinematics information. The effectiveness of our method is demonstrated with state-of-the-art results on the public JIGSAWS dataset.
  arXiv  Detail & Related papers  (2020-11-03T11:00:10Z)
• Object recognition through pose and shape estimation [0.0]
  Computer vision helps machines or computer to see like humans. Gesture recognition and movement recognition are the current area of research in computer vision.
  arXiv  Detail & Related papers  (2020-06-23T09:55:40Z)
• A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits [91.3755431537592]
  Recent evidence shows that a simulation of the children's embodied strategies can improve the machine intelligence too. This article explores the application of embodied strategies to convolutional neural network models in the context of developmental neuro-robotics.
  arXiv  Detail & Related papers  (2020-03-23T14:55:00Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)

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.