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YOLSE: Egocentric Fingertip Detection from Single RGB Images
2017
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
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In this paper, we build a new dataset named EgoGesture and propose a heatmap-based solution for fingertip detection. ...
With the development of wearable device and augmented reality (AR), the human device interaction in egocentric vi sion, especially the hand gesture based interaction, has attracted lots of attention among ...
We believe our dataset is diverse and representative as a benchmark dataset for the fingertip and hand related research in the egocentric vision. ...
doi:10.1109/iccvw.2017.79
dblp:conf/iccvw/WuLCZJ17
fatcat:cf3ovn5nfff7ndfka755qxj75u
Real-time Egocentric Gesture Recognition on Mobile Head Mounted Displays
[article]
2017
arXiv
pre-print
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Our main contributions are: 1) A novel mixed-reality data collection tool to automatic annotate bounding boxes and gesture labels; 2) The largest-to-date egocentric hand gesture and bounding box dataset ...
In this work, we demonstrate real-time egocentric hand gesture detection and localization on mobile HMDs. ...
There is limited dataset available on egocentric hand gestures and bounding boxes. 2. ...
arXiv:1712.04961v1
fatcat:ib6zldqqgjdofh6izbxmkx2f2i
Simultaneous Segmentation and Recognition: Towards More Accurate Ego Gesture Recognition
2019
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
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art on the EgoGesture dataset [31]. ...
While the context of an image is important for tasks like scene understanding, object recognition, image caption generation and activity recognition, it plays a minimal role in ego hand gesture recognition ...
It also captures both RGB and depth modalities. These properties of the EgoGesture dataset makes it a valid candidate for testing and benchmarking ego gesture recognition algorithms. ...
doi:10.1109/iccvw.2019.00537
dblp:conf/iccvw/ChalasaniS19
fatcat:ovgczj4zzfclphjzuu4invmpry
Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks
[article]
2019
arXiv
pre-print
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We evaluate our architecture on two publicly available datasets - EgoGesture and NVIDIA Dynamic Hand Gesture Datasets - which require temporal detection and classification of the performed hand gestures ...
Real-time recognition of dynamic hand gestures from video streams is a challenging task since (i) there is no indication when a gesture starts and ends in the video, (ii) performed gestures should only ...
ACKNOWLEDGEMENTS We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. ...
arXiv:1901.10323v3
fatcat:dndlkm2mczbijfrgrwpskrixqi
Editorial IEEE Transactions on Multimedia Special Section on Video Analytics: Challenges, Algorithms, and Applications
2018
IEEE transactions on multimedia
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After 2 rounds of rigorous review, we have 3 high quality research papers as part of this special section: r Another work on "EgoGesture: A New Dataset and Benchmark for Egocentric Hand Gesture Recognition ...
" presents a dataset with more than 24,000 gesture samples and 3,000,000 frames for both color and depth modalities from 50 distinct subjects giving 80+ different gestures in 6 diverse indoor and outdoor ...
doi:10.1109/tmm.2018.2821959
fatcat:54figgi6tfgjvizszssj7i7oqq
Multi-Task and Multi-Modal Learning for RGB Dynamic Gesture Recognition
2021
IEEE Sensors Journal
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2021 pre-print arXiv:2110.15639v1 fatcat:grcrgpdtl5gmhjtiiujfld5tyy
Our framework is trained to learn a representation for multi-task learning: gesture segmentation and gesture recognition. ...
Experimental results on three public gesture recognition datasets show that our proposed method provides superior performance compared with existing gesture recognition frameworks. ...
(a) is the EgoGesture dataset, including indoor and outdoor environments for egocentric hand gesture recognition. ...
doi:10.1109/jsen.2021.3123443
fatcat:4biyoph3xbe6dksji53pzpcc6i
Simultaneous Segmentation and Recognition: Towards more accurate Ego Gesture Recognition
[article]
2019
arXiv
pre-print
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art on the EgoGesture dataset ...
While the context of an image is important for tasks like scene understanding, object recognition, image caption generation and activity recognition, it plays a minimal role in ego hand gesture recognition ...
It also captures both RGB and depth modalities. These properties of the EgoGesture dataset makes it a valid candidate for testing and benchmarking ego gesture recognition algorithms. ...
arXiv:1909.08606v1
fatcat:h3jctvaxlrbjda2rtb6homtily
Enhanced Self-Perception in Mixed Reality: Egocentric Arm Segmentation and Database with Automatic Labelling
[article]
2020
arXiv
pre-print
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of this database, we report results on different real egocentric hand datasets, including GTEA Gaze+, EDSH, EgoHands, Ego Youtube Hands, THU-Read, TEgO, FPAB, and Ego Gesture, which allow for direct comparisons ...
The main contributions of this work are: i) a comprehensive survey of segmentation algorithms for AV; ii) an Egocentric Arm Segmentation Dataset, composed of more than 10, 000 images, comprising variations ...
For instance, The EPIC-KITCHENS Dataset is not providing segmentation masks [16] ; the Egocentric Gesture Recognition dataset [10] only provide segmentation masks for chroma-key hand gesture images; ...
arXiv:2003.12352v1
fatcat:gfnhmhhijjhn7d66fzmsdimj7u
Content-Adaptive and Attention-Based Network for Hand Gesture Recognition
2022
Applied Sciences
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The proposed network achieved 83.2% and 93.8% recognition accuracy on two publicly available benchmark datasets, NVGesture and EgoGesture datasets, respectively. ...
For hand gesture recognition, recurrent neural networks and 3D convolutional neural networks are the most commonly used methods for learning the spatial–temporal features of gestures. ...
The EgoGesture dataset is a large-scale dataset for egocentric hand gesture recognition, consisting of 83 classes collected from four indoor and two outdoor scenes. ...
doi:10.3390/app12042041
fatcat:whjvcjomtnc55ofubqg6ioessy
IPN Hand: A Video Dataset and Benchmark for Real-Time Continuous Hand Gesture Recognition
[article]
2020
arXiv
pre-print
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In this paper, we introduce a new benchmark dataset named IPN Hand with sufficient size, variety, and real-world elements able to train and evaluate deep neural networks. ...
The experimental results show that the state-of-the-art ResNext-101 model decreases about 30% accuracy when using our real-world dataset, demonstrating that the IPN Hand dataset can be used as a benchmark ...
ACKNOWLEDGMENT This work was supported by JSPS KAKENHI Grant Number 15H05915, 17H01745, 17H06100 and 19H04929. ...
arXiv:2005.02134v2
fatcat:qclawbhdxrd33nucnktpdrkwy4
Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition
[article]
2020
arXiv
pre-print
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Comprehensive experiments are performed on three benchmark datasets (IsoGD, NvGesture, and EgoGesture), demonstrating the state-of-the-art performance in both single- and multi-modality settings.The code ...
gesture recognition. ...
For fair evaluations with SOTA methods, infrared modality is not used in our experiments. The EgoGesture dataset [4] is a large multi-modal egocentric hand gesture dataset. ...
arXiv:2008.09412v1
fatcat:vphe2saxbjhxtee2twdkbby3yi
Real Time Egocentric Object Segmentation: THU-READ Labeling and Benchmarking Results
[article]
2021
arXiv
pre-print
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Due to the lack of datasets of pixel-wise annotations of egocentric objects, in this paper we contribute with a semantic-wise labeling of a subset of 2124 images from the RGB-D THU-READ Dataset. ...
While most previous works have been focused on segmenting egocentric human body parts (mainly hands), little attention has been given to egocentric objects. ...
To the best of our knowledge, there exist only three published egocentric and RGB-D datasets including human body parts (and) objects: Ego Gesture [11] , created for the task of hand gesture recognition ...
arXiv:2106.04957v1
fatcat:snjrhnz6zbdzre5bo2tu3yw3gu
Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition With Multimodal Training
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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We present an efficient approach for leveraging the knowledge from multiple modalities in training unimodal 3D convolutional neural networks (3D-CNNs) for the task of dynamic hand gesture recognition. ...
Experimental results show that our framework improves the test time recognition accuracy of unimodal networks, and provides the state-of-the-art performance on various dynamic hand gesture recognition ...
, 51] is a large multimodal hand gesture dataset collected for the task of egocentric gesture recognition. ...
doi:10.1109/cvpr.2019.00126
dblp:conf/cvpr/AbavisaniJP19
fatcat:wmodjfndpzcaxb32ntq7xyxk44
Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition with Multimodal Training
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1812.06145v1 fatcat:tx6w4s6xsbbdzmq3lhrejchd3e
We present an efficient approach for leveraging the knowledge from multiple modalities in training unimodal 3D convolutional neural networks (3D-CNNs) for the task of dynamic hand gesture recognition. ...
Experimental results show that our framework improves the test time recognition accuracy of unimodal networks, and provides the state-of-the-art performance on various dynamic hand gesture recognition ...
The incorporation of our method for multimodal learning in other applications is a topic of further research. ...
arXiv:1812.06145v2
fatcat:zxvwvfb34zfs7d47qbtldb3sri
Predicting the Future from First Person (Egocentric) Vision: A Survey
[article]
2021
arXiv
pre-print
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The research in egocentric video analysis is developing rapidly thanks to the increasing availability of wearable devices and the opportunities offered by new large-scale egocentric datasets. ...
Egocentric videos can bring a lot of information about how humans perceive the world and interact with the environment, which can be beneficial for the analysis of human behaviour. ...
The dataset also provides hand masks and gaze annotations. EgoGesture [96] : A dataset designed for gesture recognition, consisting of 24.161 videos of 50 subjects performing 83 different gestures. ...
arXiv:2107.13411v1
fatcat:viullzimvzc33espfjurvdisia
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