A multimodal gesture recognition dataset for desktop human-computer interaction

• URL: http://arxiv.org/abs/2401.03828v1
• Date: Mon, 8 Jan 2024 11:35:25 GMT
• Title: A multimodal gesture recognition dataset for desktop human-computer interaction
• Authors: Qi Wang, Fengchao Zhu, Guangming Zhu, Liang Zhang, Ning Li, Eryang Gao
• Abstract summary: GR4DHCI comprises over 7,000 gesture samples and a total of 382,447 frames for both Stereo IR and skeletal modalities. GR4DHCI comprises over 7,000 gesture samples and a total of 382,447 frames for both Stereo IR and skeletal modalities. We also address the variances in hand positioning during desktop interactions by incorporating 27 different hand positions into the dataset.
• Score: 10.053600460554234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gesture recognition is an indispensable component of natural and efficient human-computer interaction technology, particularly in desktop-level applications, where it can significantly enhance people's productivity. However, the current gesture recognition community lacks a suitable desktop-level (top-view perspective) dataset for lightweight gesture capture devices. In this study, we have established a dataset named GR4DHCI. What distinguishes this dataset is its inherent naturalness, intuitive characteristics, and diversity. Its primary purpose is to serve as a valuable resource for the development of desktop-level portable applications. GR4DHCI comprises over 7,000 gesture samples and a total of 382,447 frames for both Stereo IR and skeletal modalities. We also address the variances in hand positioning during desktop interactions by incorporating 27 different hand positions into the dataset. Building upon the GR4DHCI dataset, we conducted a series of experimental studies, the results of which demonstrate that the fine-grained classification blocks proposed in this paper can enhance the model's recognition accuracy. Our dataset and experimental findings presented in this paper are anticipated to propel advancements in desktop-level gesture recognition research.

Related papers

• DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action Recognition [51.96660522869841]
  DailyDVS-200 is a benchmark dataset tailored for the event-based action recognition community. It covers 200 action categories across real-world scenarios, recorded by 47 participants, and comprises more than 22,000 event sequences. DailyDVS-200 is annotated with 14 attributes, ensuring a detailed characterization of the recorded actions.
  arXiv  Detail & Related papers  (2024-07-06T15:25:10Z)
• Advancing Location-Invariant and Device-Agnostic Motion Activity Recognition on Wearable Devices [6.557453686071467]
  We conduct a comprehensive evaluation of the generalizability of motion models across sensor locations. Our analysis highlights this challenge and identifies key on-body locations for building location-invariant models. We present deployable on-device motion models reaching 91.41% frame-level F1-score from a single model irrespective of sensor placements.
  arXiv  Detail & Related papers  (2024-02-06T05:10:00Z)
• Sketch Input Method Editor: A Comprehensive Dataset and Methodology for Systematic Input Recognition [14.667745062352148]
  This study aims to create a Sketch Input Method Editor (SketchIME) specifically designed for a professional C4I system. Within this system, sketches are utilized as low-fidelity prototypes for recommending standardized symbols. By incorporating few-shot domain adaptation and class-incremental learning, the network's ability to adapt to new users is significantly enhanced.
  arXiv  Detail & Related papers  (2023-11-30T05:05:38Z)
• The Impact of Different Backbone Architecture on Autonomous Vehicle Dataset [120.08736654413637]
  The quality of the features extracted by the backbone architecture can have a significant impact on the overall detection performance. Our study evaluates three well-known autonomous vehicle datasets, namely KITTI, NuScenes, and BDD, to compare the performance of different backbone architectures on object detection tasks.
  arXiv  Detail & Related papers  (2023-09-15T17:32:15Z)
• Online Recognition of Incomplete Gesture Data to Interface Collaborative Robots [0.0]
  This paper introduces an HRI framework to classify large vocabularies of interwoven static gestures (SGs) and dynamic gestures (DGs) captured with wearable sensors. The recognized gestures are used to teleoperate a robot in a collaborative process that consists of preparing a breakfast meal.
  arXiv  Detail & Related papers  (2023-04-13T18:49:08Z)
• DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition [35.642868929840034]
  Gait recognition is a biometric technology that recognizes the identity of humans through their walking patterns. We propose a novel and high-performance framework named DyGait to focus on the extraction of dynamic features. Our network achieves an average Rank-1 accuracy of 71.4% on the GREW dataset, 66.3% on the Gait3D dataset, 98.4% on the CASIA-B dataset and 98.3% on the OU-M dataset.
  arXiv  Detail & Related papers  (2023-03-27T07:36:47Z)
• Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances [76.34037366117234]
  We introduce a new dataset called Robot Control Gestures (RoCoG-v2) The dataset is composed of both real and synthetic videos from seven gesture classes. We present results using state-of-the-art action recognition and domain adaptation algorithms.
  arXiv  Detail & Related papers  (2023-03-17T23:23:55Z)
• Video-based Pose-Estimation Data as Source for Transfer Learning in Human Activity Recognition [71.91734471596433]
  Human Activity Recognition (HAR) using on-body devices identifies specific human actions in unconstrained environments. Previous works demonstrated that transfer learning is a good strategy for addressing scenarios with scarce data. This paper proposes using datasets intended for human-pose estimation as a source for transfer learning.
  arXiv  Detail & Related papers  (2022-12-02T18:19:36Z)
• Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based Baseline [95.88825497452716]
  Gait benchmarks empower the research community to train and evaluate high-performance gait recognition systems. GREW is the first large-scale dataset for gait recognition in the wild. SPOSGait is the first NAS-based gait recognition model.
  arXiv  Detail & Related papers  (2022-05-05T14:57:39Z)
• Skeleton-Based Mutually Assisted Interacted Object Localization and Human Action Recognition [111.87412719773889]
  We propose a joint learning framework for "interacted object localization" and "human action recognition" based on skeleton data. Our method achieves the best or competitive performance with the state-of-the-art methods for human action recognition.
  arXiv  Detail & Related papers  (2021-10-28T10:09:34Z)
• Force myography benchmark data for hand gesture recognition and transfer learning [5.110894308882439]
  We contribute to the advancement of this field by making accessible a benchmark dataset collected using a commercially available sensor setup from 20 persons covering 18 unique gestures. We illustrate one use-case for such data, showing how we can improve gesture recognition accuracy by utilising transfer learning to incorporate data from multiple other persons.
  arXiv  Detail & Related papers  (2020-07-29T15:43:59Z)

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.