OUHANDS database for hand detection and pose recognition

Matilainen, Matti; Sangi, Pekka; Holappa, Jukka; Silvén, Olli

doi:10.1109/ipta.2016.7821025

Cited by 49 publications

(22 citation statements)

References 23 publications

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“…We have collected a combined dataset containing a total of 24,535 images and over 41,000 hand instances. To ensure the diversity of the data collected, the dataset combines samples from different datasets, (e.g., those from [12,24,44,45]) and other images sources. In addition, we are more interested in creating a realistic and diverse dataset in terms of viewpoints (first and third person views, etc.…”

Section: Experiments and Discussionmentioning

confidence: 99%

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A Deep Learning-Based End-to-End Composite System for Hand Detection and Gesture Recognition

Mohammed

Islam

2019

Sensors

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Recent research on hand detection and gesture recognition has attracted increasing interest due to its broad range of potential applications, such as human-computer interaction, sign language recognition, hand action analysis, driver hand behavior monitoring, and virtual reality. In recent years, several approaches have been proposed with the aim of developing a robust algorithm which functions in complex and cluttered environments. Although several researchers have addressed this challenging problem, a robust system is still elusive. Therefore, we propose a deep learning-based architecture to jointly detect and classify hand gestures. In the proposed architecture, the whole image is passed through a one-stage dense object detector to extract hand regions, which, in turn, pass through a lightweight convolutional neural network (CNN) for hand gesture recognition. To evaluate our approach, we conducted extensive experiments on four publicly available datasets for hand detection, including the Oxford, 5-signers, EgoHands, and Indian classical dance (ICD) datasets, along with two hand gesture datasets with different gesture vocabularies for hand gesture recognition, namely, the LaRED and TinyHands datasets. Here, experimental results demonstrate that the proposed architecture is efficient and robust. In addition, it outperforms other approaches in both the hand detection and gesture classification tasks.

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Section: Experiments and Discussionmentioning

confidence: 99%

“…The utilization of hand-crafted features has dominated early research in hand detection and gesture recognition. Most of these approaches have utilized hand skin color, texture, and appearance features for hand detection and gesture recognition [8,9,10,11,12,13]. However, their success is only found in certain well-prepared environments.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based End-to-End Composite System for Hand Detection and Gesture Recognition

Mohammed

Islam

2019

Sensors

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“…This dataset (MATILAINEN et al, 2016) is aimed for evaluation of both, classification and segmentation methods. It contains manually segmented binary masks, as well as aligned depth and color frames.…”

Section: Ouhandsmentioning

confidence: 99%

“…The datasets used in this work contain gestures from more than one subject (BARCZAK et al, 2011;HSIAO et al, 2014;MATILAINEN et al, 2016). This makes it possible to use two different validation techniques:…”

Section: Validation Techniquementioning

confidence: 99%

A convolutional neural network with feature fusion for real-time hand posture recognition

Chevtchenko

Vale

Macario

et al. 2018

Applied Soft Computing

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“…The proposed method is evaluated on two benchmark datasets, i.e. OUHANDS [23] and HGR1 [24]. Our experiments show that by using a robust hand segmentation/recognition architecture, supported by an efficient data augmentation technique, we can develop a robust RGB‐based HGR system, which has excellent performance in uncontrolled and unseen scenarios, at much lower training and computational costs than the current state‐of‐the‐art.…”

Section: Introductionmentioning

confidence: 99%

HGR‐Net: a fusion network for hand gesture segmentation and recognition

Dadashzadeh

Targhi

Tahmasbi

et al. 2019

IET Computer Vision

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Robust recognition of hand gestures in real-world applications is still an unaccomplished goal due to many remaining challenges, such as cluttered backgrounds and unconstrained environmental factors. In most existing methods in this field, hand segmentation is a critical step to reduce redundant information in the scene background, in preparation for the hand gesture recognition stage. Therefore, we propose a new two-stage convolutional neural network (CNN) architecture, called HGR-Net, where the first stage performs accurate pixel-level semantic segmentation to determine the hand regions, and the second stage identifies the hand gesture. The segmentation stage architecture is based on the combination of fully convolutional residual network and atrous spatial pyramid pooling. Although the segmentation sub-network is trained without depth information, it is significantly robust against challenges such as illumination variations and complex backgrounds. The recognition stage deploys a two-stream CNN which fuses the information from the RGB and segmented images by combining their deep representations in a new fully connected layer before classification. Extensive experiments on public hand gesture datasets show that our deep architecture achieves almost as good as state-of-the-art performance in segmentation and recognition of static hand gestures, at a fraction of training time, run time, and model size.

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OUHANDS database for hand detection and pose recognition

Cited by 49 publications

References 23 publications

A Deep Learning-Based End-to-End Composite System for Hand Detection and Gesture Recognition

A Deep Learning-Based End-to-End Composite System for Hand Detection and Gesture Recognition

A convolutional neural network with feature fusion for real-time hand posture recognition

HGR‐Net: a fusion network for hand gesture segmentation and recognition

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