ChaLearn Looking at People Challenge 2014: Dataset and Results

The pascal visual object classes (VOC) challenge

Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599

Learning realistic human actions from movies

Ivan Laptev, A., Marszałek, M., Schmid, C., & Rozenfeld, B. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587756 · Cited by 3,481

Action Recognition with Improved Trajectories

Wang, H., & Schmid, C. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.441 · Cited by 3,407

Recognizing human actions: A local SVM approach

Schüldt, C., Laptev, I., & Barbara Caputo, B. (2004) · Cited by 2,908

2D Human Pose Estimation: New Benchmark and State of the Art Analysis

Andriluka, M., Pishchulin, L., Gehler, P., & Schiele, B. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.471 · Cited by 2,699

Aggregating Local Image Descriptors into Compact Codes

Jegou, H., Perronnin, F., Douze, M., Sanchez, J., Perez, P., & Schmid, C. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.235 · Cited by 1,503

Poselets: Body part detectors trained using 3D human pose annotations

Bourdev, L., & Malik, J. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459303 · Cited by 961

Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation

Johnson, S., & Everingham, M. (2010) · British Machine Vision Conference (BMVC) · doi:10.5244/c.24.12 · Cited by 870

Articulated Human Detection with Flexible Mixtures of Parts

Yang, Y., & Ramanan, D. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.261 · Cited by 843

Progressive search space reduction for human pose estimation

Ferrari, V., Marín-Jiménez, M. J., & Zisserman, A. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587468 · Cited by 613

Deep-Aligned Convolutional Neural Network for Skeleton-Based Action Recognition and Segmentation

Hosseini, B., Montagné, R., & Hammer, B. (2019) · Data Science and Engineering · doi:10.1007/s41019-020-00123-3 · Cited by 22

Sequentially Supervised Long Short-Term Memory for Gesture Recognition

Wang, P., Song, Q., Han, H., & Cheng, J. (2016) · Cognitive Computation · doi:10.1007/s12559-016-9388-6 · Cited by 21

Cultural event recognition by subregion classification with convolutional neural network

Park, S., & Kwak, N. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301335 · Cited by 20

Snapture—a Novel Neural Architecture for Combined Static and Dynamic Hand Gesture Recognition

Ali, H., Jirak, D., & Wermter, S. (2023) · Cognitive Computation · doi:10.1007/s12559-023-10174-z · Cited by 20

Multi-modality Gesture Detection and Recognition with Un-supervision, Randomization and Discrimination

Chen, G., Clarke, D., Giuliani, M., Gaschler, A., Wu, D., Weikersdorfer, D., & Knoll, A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16178-5_43 · Cited by 19

Action Recognition from RGB-D Data: Comparison and Fusion of Spatio-Temporal Handcrafted Features and Deep Strategies

Asadi, M., Bertiche, H., Roig, V., Kasaei, S., & Escalera, S. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.376 · Cited by 19

Evaluation of hidden Markov models using deep CNN features in isolated sign recognition

Tur, A. O., & Keleş, H. Y. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10593-w · Cited by 17

SIGNIFY: Leveraging Machine Learning and Gesture Recognition for Sign Language Teaching Through a Serious Game

Ulrich, L., Carmassi, G., Garelli, P., Presti, G. L., Ramondetti, G., Marullo, G., Innocente, C., & Vezzetti, E. (2024) · Future Internet · doi:10.3390/fi16120447 · Cited by 17

Human‐action recognition using a multi‐layered fusion scheme of Kinect modalities

Seddik, B., Gazzah, S., & Amara, N. E. B. (2017) · IET Computer Vision · doi:10.1049/iet-cvi.2016.0326 · Cited by 17

Action detection fusing multiple Kinects and a WIMU: an application to in-home assistive technology for the elderly

Clapés, A., Pardo, À., Pujol, O., & Escalera, S. (2018) · Machine Vision and Applications · doi:10.1007/s00138-018-0931-1 · Cited by 16

A comprehensive survey on RGB-D-based human action recognition: algorithms, datasets, and popular applications

Zhang, Y., & Wang, Y. (2025) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-025-00677-0 · Cited by 23

(2+1)D-SLR: an efficient network for video sign language recognition

Wang, F., Du, Y., Wang, G., Zeng, Z., & Zhao, L. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06467-9 · Cited by 16

Specificity and Latent Correlation Learning for Action Recognition Using Synthetic Multi-View Data From Depth Maps

Liang, B., & Zheng, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2740122 · Cited by 16

Back to the beginning: Starting point detection for early recognition of ongoing human actions

Wang, B., & Hoai, M. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.10.001 · Cited by 16

ChaLearn looking at people 2015 new competitions: Age estimation and cultural event recognition

Escalera, S., Gonzàlez, J., Baró, X., Pardo, P. L., Fabián, J., Oliu, M., Escalante, H. J., Huerta, I., & Guyon, I. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280614 · Cited by 15

Progression Modelling for Online and Early Gesture Detection

Gupta, V., Dwivedi, S. K., Dabral, R., & Jain, A. (2019) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2019.00040 · Cited by 15

A semi-automatic annotation tool for unobtrusive gesture analysis

Beugher, S. D., Brône, G., & Goedemé, T. (2017) · Language Resources and Evaluation · doi:10.1007/s10579-017-9404-9 · Cited by 15

Isolated Sign Recognition with a Siamese Neural Network of RGB and Depth Streams

Tur, A. O., & Keleş, H. Y. (2019) · IEEE EUROCON - International Conference on Computer as a Tool (EUROCON) · doi:10.1109/eurocon.2019.8861945 · Cited by 15

Action Detection with Improved Dense Trajectories and Sliding Window

Shu, Z., Yun, K., & Samaras, D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16178-5_38 · Cited by 15

Proceedings of the Austrian Robotics Workshop 2018

Zech, P., & Piater, J. (2018) · doi:10.15203/3187-22-1 · Cited by 15

Multi-modal Gesture Recognition Using Skeletal Joints and Motion Trail Model

Liang, B., & Zheng, L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16178-5_44 · Cited by 14

Exploring Fisher vector and deep networks for action spotting

Wang, Z., Wang, L., Du, W., & Qiao, Y. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301330 · Cited by 13

Real-Time Sign Language Recognition Based on Video Stream

Zhao, K., Zhang, K., Zhai, Y. J., Wang, D., & Su, J. (2020) · Chinese Control Conference (CCC) · doi:10.23919/ccc50068.2020.9188508 · Cited by 13

Multi-View Hand-Hygiene Recognition for Food Safety

Zhong, C., Reibman, A. R., Mina, H. A., & Deering, A. J. (2020) · Journal of Imaging · doi:10.3390/jimaging6110120 · Cited by 13

Indian Sign Language Recognition through Hybrid ConvNet-LSTM Networks

Muthu Mariappan, H., & Gomathi, V. (2021) · EMITTER International Journal of Engineering Technology · doi:10.24003/emitter.v9i1.613 · Cited by 14

Modout: Learning Multi-Modal Architectures by Stochastic Regularization

Li, F., Neverova, N., Wolf, C., & Taylor, G. W. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.59 · Cited by 13

Multimodal Gesture Recognition Using Densely Connected Convolution and BLSTM

Li, D., Chen, Y., Gao, M., Jiang, S., & Huang, C. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545502 · Cited by 13

Motion Recognition Employing Multiple Kernel Learning of Fisher Vectors Using Local Skeleton Features

Goutsu, Y., Takano, W., & Nakamura, Y. (2015) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2015.50 · Cited by 12

Temporal Hierarchical Dictionary Guided Decoding for Online Gesture Segmentation and Recognition

Chen, H., Liu, X., Shi, J., & Zhao, G. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3028962 · Cited by 12

Continuous hand gesture recognition: Benchmarks and methods

Emporio, M., Ghasemaghaei, A., LaViola, J. J., & Giachetti, A. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104435 · Cited by 11

Modout: Learning to Fuse Modalities via Stochastic Regularization

Li, F., Neverova, N., Wolf, C., & Taylor, G. W. (2016) · Journal of Computational Vision and Imaging Systems · doi:10.15353/vsnl.v2i1.103 · Cited by 11

Gesture Recognition Using Enhanced Depth Motion Map and Static Pose Map

Zhang, Z., Wei, S., Song, Y., & Zhang, Y. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.38 · Cited by 11

Comparing Hybrid NN-HMM and RNN for Temporal Modeling in Gesture Recognition

Granger, N., & El‐Yacoubi, M. A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70096-0_16 · Cited by 11

Hidden States Exploration for 3D Skeleton-Based Gesture Recognition

Liu, X., Shi, H., Hong, X., Chen, H., Tao, D., & Zhao, G. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00201 · Cited by 13

Learning deep and compact models for gesture recognition

Mullick, K., & Namboodiri, A. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8297033 · Cited by 12

3D Skeletal Gesture Recognition via Sparse Coding of Time-Warping Invariant Riemannian Trajectories

Liu, X., & Zhao, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-05710-7_56 · Cited by 11

Text-Aligned Radar-Based Sign Language Recognition for Healthcare Communication

Mineo, R., Sorrenti, A., Caligiore, G., Salanitri, F. P., Bellitto, G., Polikovsky, S., Fontana, S., Ragonese, E., Spampinato, C., & Palazzo, S. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00513 · Cited by 10

Predicting Body Movement and Recognizing Actions: An Integrated Framework for Mutual Benefits

Wang, B., & Hoai, M. (2018) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2018.00056 · Cited by 10

Evolving weighting schemes for the Bag of Visual Words

Escalante, H. J., Ponce-López, V., Escalera, S., Baró, X., Morales-Reyes, A., & Martínez-Carranza, J. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2223-x · Cited by 10

Machine learning for video event recognition

Avola, D., Cascio, M., Cinque, L., Foresti, G. L., & Pannone, D. (2021) · Integrated Computer-Aided Engineering · doi:10.3233/ica-210652 · Cited by 9

Localizing salient body motion in multi-person scenes using convolutional neural networks

Letsch, F., Jirak, D., & Wermter, S. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.11.048 · Cited by 10

A CNN Bi-LSTM based Multimodal Continuous Hand Gesture Recognition

Nayan, N., Ghosh, D., & Pradhan, P. M. (2022) · IEEE India Council International Subsections Conference (INDISCON) · doi:10.1109/indiscon54605.2022.9862878 · Cited by 8

Human Motion Segmentation via Velocity-Sensitive Dual-Side Auto-Encoder

Bai, Y., Wang, L., Liu, Y., Yin, Y., Di, H., & Fu, Y. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3217720 · Cited by 8

Multimodal gesture recognition

Katsamanis, A., Pitsikalis, V., Theodorakis, S., & Maragos, P. A. (2017) · ACM eBooks · doi:10.1145/3015783.3015796 · Cited by 8

Temporal Hierarchical Dictionary with HMM for Fast Gesture Recognition

Chen, H., Liu, X., & Zhao, G. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8546245 · Cited by 8

Two Stage Continuous Gesture Recognition Based on Deep Learning

Wang, H. (2021) · Electronics · doi:10.3390/electronics10050534 · Cited by 8

Application of Deep Learning Techniques on Sign Language Recognition—A Survey

Barve, P., Mutha, N., Kulkarni, A., Nigudkar, Y., & Robert, Y. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-2934-1_14 · Cited by 9

Particle Filter Based Probabilistic Forced Alignment for Continuous Gesture Recognition

Camgöz, N. C., Hadfield, S., & Bowden, R. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.364 · Cited by 8

Spatio-temporal pyramid cuboid matching for action recognition using depth maps

Liang, B., & Zheng, L. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351165 · Cited by 7

Supporting One-Time Point Annotations for Gesture Recognition

Nguyen-Dinh, L.-V., Calatroni, A., & Tröster, G. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2637350 · Cited by 7

Cultural Event Recognition with Visual ConvNets and Temporal ModelsPreprint

Salvador, A., Zeppelzauer, M., Vizuete, D. M., Calafell, A., & Giró-i-Nieto, X. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.06567 · Cited by 9

LiveTraj

Fu, T. Z. J., Ding, J., Ma, R. T. B., Winslett, M., Yang, Y., Zhang, Z., Pei, Y., & Ni, B. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2807401 · Cited by 6

Multi-View Fusion for Sign Language Recognition through Knowledge Transfer Learning

Gao, L., Zhu, L., Xue, S., Wan, L., Li, P., & Feng, W. (2022) · ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry (VRCAI) · doi:10.1145/3574131.3574434 · Cited by 6

Modalities Combination for Italian Sign Language Extraction and Recognition

Seddik, B., Gazzah, S., & Amara, N. E. B. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23234-8_65 · Cited by 6

Adversarial multi-task deep learning for signer-independent feature representation

Fang, Y., Xiao, Z., Cai, S., & Ni, L. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03649-3 · Cited by 6

Augmented skeletal joints for temporal segmentation of sign language actions

Seddik, B., Gazzah, S., Château, T., & Amara, N. E. B. (2014) · IEEE International Conference on Image Processing, Applications and Systems (IPAS) · doi:10.1109/ipas.2014.7043295 · Cited by 6

Spatio-temporal Human Action Localisation and Instance Segmentation in Temporally Untrimmed VideosPreprint

Saha, S., Singh, G., Sapienza, M., Torr, P. H. S., & Fabio Cuzzolin, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.07213 · Cited by 6

Advancements and challenges in vision-based sign language recognition: A comprehensive review

Trabelsi, L., Harrouch, H., Mohamed, S., Jebali, M., & Sharma, A. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103626 · Cited by 6

Studying Multi-modal Human Robot Interaction Using a Mobile VR Simulation

Milde, S., Runzheimer, T., Friesen, S., Peiffer, J.-H., Höfler, J.-J., Geis, K., Milde, J., & Blum, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35602-5_11 · Cited by 5

Informing Machine Perception With Psychophysics [Point of View]

Dulay, J., Poltoratski, S., Hartmann, T. S., Anthony, S. E., & Scheirer, W. J. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2024.3380905 · Cited by 5

Hands, face and joints for multi-modal human-action temporal segmentation and recognition

Seddik, B., Gazzah, S., & Amara, N. E. B. (2015) · European Signal Processing Conference (EUSIPCO) · doi:10.1109/eusipco.2015.7362562 · Cited by 5

Dual-Side Auto-Encoder for High-Dimensional Time Series Segmentation

Bai, Y., Wang, L., Liu, Y., Yin, Y., & Fu, Y. (2020) · Proceedings · doi:10.1109/icdm50108.2020.00102 · Cited by 5

Recognizing Words from Gestures: Discovering Gesture Descriptors Associated with Spoken Utterances

Okada, S., & Otsuka, K. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.60 · Cited by 4

Deep learning models beyond temporal frame-wise features for hand gesture video recognition

Mira, A., & Hellwich, O. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-05910-7 · Cited by 4

Multi-modal Sign Language Recognition with Enhanced Spatiotemporal Representation

Xiao, S., Fang, Y., & Ni, L. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533707 · Cited by 4

Improving bag of visual words representations with genetic programming

Escalante, H. J., Martínez-Carranza, J., Escalera, S., Ponce-López, V., & Baró, X. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280799 · Cited by 3

Gesture-Based Machine Learning for Enhanced Autonomous Driving: A Novel Dataset and System Integration Approach

Milde, S., Friesen, S., Runzheimer, T., Beilstein, C., Blum, R., & Milde, J. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-61963-2_24 · Cited by 3

A multimedia gesture dataset for human robot communication: Acquisition, tools and recognition results

Rodomagoulakis, I., Kardaris, N., Pitsikalis, V., Arvanitakis, A. D., & Maragos, P. A. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532923 · Cited by 3

A review of sign language recognition research

Yu, M., Jia, J., Xue, C., Yan, G., Guo, Y., & Liu, Y. (2022) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-210050 · Cited by 3

Sign Language Interpretation Using Deep Learning

Mallika, S. S., Sanjana, A., Gayatri, A., & Sai, S. V. N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-36402-0_64 · Cited by 4

A Collaborative Multi-modal Fusion Method Based on Random Variational Information Bottleneck for Gesture Recognition

Gu, Y., Li, Y., Chen, Y., Wang, J., & Shen, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67832-6_6 · Cited by 3

CentralNet: a Multilayer Approach for Multimodal Fusion

Vielzeuf, V., Lechervy, A., Pateux, S., & Jurie, F. (2018) · HAL · doi:10.48550/arxiv.1808.07275 · Cited by 3

Sign Language Recognition Using Machine Learning

Chowdary, A. U., Sree, C. M., & Siddhartha, B. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.49199 · Cited by 3

Visual Methods for Sign Language Recognition: A Modality-Based Review

Seddik, B., & Amara, N. E. B. (2021) · Smart sensors, measurement and instrumentation · doi:10.1007/978-3-030-81982-8_9 · Cited by 2

Real-Time Hand Gesture Recognition Using Depth Information and Path Analysis

Wu, Y.-J., & Ding, J. (2023) · IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) · doi:10.1109/ecice59523.2023.10383019 · Cited by 2

Variable Scale Gesture Recognition: A Dataset and Comprehensive Analysis

Li, Z., Xu, T., Yang, X., Sun, J., & Zhu, G. (2023) · International Conference on Robotics, Automation and Intelligent Control (ICRAIC) · doi:10.1109/icraic61978.2023.00027 · Cited by 2

Hand and Face Segmentation with Deep Convolutional Networks using Limited Labelled Data

Sincan, Ö. M., Gencoglu, S., Bacak, M., & Keleş, H. Y. (2019) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit.2019.8932835 · Cited by 2

A Transfer Learning Based Decision Level Multimodal Framework for Continuous Sign Language Recognition

Nayan, N., Ghosh, D., & Pradhan, P. M. (2025) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon66050.2025.11375073 · Cited by 1

SIGN LANGUAGE INTERPRETER

Keerthi, K., Gnapika, K., Reddy, P. M., Reddy, B. I., & Veena, K. (2023) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem18699 · Cited by 1

A Detail Study of Sign Language Communication for Deaf-Mute to Normal Person

Mansangbhai, C. P., & Vaghela, D. B. (2024) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/150116 · Cited by 1

Synthetic Face Generation Through Eyes-to-Face Inpainting

Hassanpour, A., Mobarakeh, S. A. M., Daryani, A. E., Ramachandra, R., & Yang, B. (2023) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb57857.2023.10449216 · Cited by 1

Space–Time Signal Analysis and the 3D Shearlet Transform

Malafronte, D., Vito, E. D., & Odone, F. (2018) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-018-0791-3 · Cited by 1

Extension of the Action Verb Corpus for Supervised Learning

Hirschmanner, M., Gross, S., Krenn, B., Neubarth, F., Trapp, M., Zillich, M., & Vincze, M. (2018) · doi:10.15203/3187-22-1-13 · Cited by 1

An Efficient Method for Sign Language Recognition from Image Using Convolutional Neural Network

Kotarski, S., & Maleszka, B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-98678-4_12 · Cited by 1

Motion-Pose Recurrent Neural Network with Instantaneous Kinematic Descriptor for Skeleton Based Gesture Detection and Recognition

Zhang, Z., Song, Y., & Zhang, Y. (2017) · Asian Conference on Pattern Recognition · doi:10.1109/acpr.2017.14 · Cited by 1

Dynamic Recognition and Classification of Trajectories in SLRecon Adopted Artificial Intelligence in Kinect

Gavilanez, T. S., Gómez, E. A., Ruiz, E. P. E., & Thirumuruganandham, S. P. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-86702-7_8 · Cited by 1

A Comprehensive Survey on Hand Gesture Recognition Using Deep Learning for Electronic Device InteractionPreprint

Sinha, B. (2025) · doi:10.36227/techrxiv.175424001.19351767/v1 · Cited by 1

Extending procrustes analysis : building multi-view 2-D models from 3-D human shape samples

Perez-Sala, X. (2015) · doi:10.5821/dissertation-2117-95700

Gesture recognition with application to human-robot interaction

Mangera, R. (2015) · Open University of Cape Town (University of Cape Town)

Human Multi-Robot Interaction based on Gesture Recognition

Ángulo, C. (2015)

Data Augmented Dynamic Time Warping for Skeletal Action Classification

Chang, J. Y., & Heo, Y. S. (2018) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2017edp7275

Robust Optimization for Deep RegressionPreprint

Belagiannis, V., Rupprecht, C., Carneiro, G., & Navab, N. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.06606 · Cited by 5

Local Spatio-Temporal Representation Using the 3D Shearlet Transform

Malafronte, D., Vito, E. D., & Odone, F. (2018) · Sampling Theory in Signal and Image Processing · doi:10.1007/bf03549613

A Multimodal Fusion Model Based on Hybrid Attention Mechanism for Gesture Recognition

Li, Y., Chen, Y., Gu, Y., & Ouyang, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-73973-7_29

A Model for Sign Language Recognition System using Deep Learning

Sahithi, G. (2022) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2022.44286

Using deep learning and computer vision to interpret sign language

Chauhan, N. K., Chikara, A. S., & Chaudhary, H. (2022) · AIP conference proceedings · doi:10.1063/5.0121486

Interpreting and visualizing the decisions of Deep Neural Networks

Aamir, A. (2023) · doi:10.53846/goediss-9848

Novel Method for Recognizing Sign Language using Regularized Extreme Learning Machine

Moorthi, K., Asokan, A., B, S. S. K., Chellammal, P., Kalpana, V., & Rastogi, R. (2023) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic56838.2023.10140610

Method for Recognizing Hand Gestures in Real- Time Across Multiple Sensors Using Machine Learning

Jayanthi, M., & Rȧjeswari, S. (2023) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite57410.2023.10183177

A Real Time Recognition System for Hand Gesture Language Based on Machine Learning Using SVM Model

Anitha, E., Sanjayram, R., Madhumita, S., Sharan, M., & Ezhil, N. (2023) · IEEE Engineering Informatics · doi:10.1109/ieeeconf58110.2023.10520384

ChaLearn Looking at People 2015 challenges: Action spotting and cultural event recognition

Baró, X., Gonzàlez, J., Fabián, J., Bautista, M. Á., Oliu, M., Escalante, H. J., Guyon, I., & Escalera, S. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301329 · Cited by 38

Results and Analysis of ChaLearn LAP Multi-modal Isolated and Continuous Gesture Recognition, and Real Versus Fake Expressed Emotions Challenges

Wan, J., Escalera, S., Anbarjafari, G., Escalante, H. J., Baró, X., Guyon, I., Madadi, M., Allïk, J., Gorbova, J., Lin, C., et al. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.377 · Cited by 77

The ChaLearn gesture dataset (CGD 2011)

Guyon, I., Athitsos, V., Jangyodsuk, P., & Escalante, H. J. (2014) · Machine Vision and Applications · doi:10.1007/s00138-014-0596-3 · Cited by 105

ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition

Wan, J., Li, S. Z., Zhao, Y., Zhou, S., Guyon, I., & Escalera, S. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.100 · Cited by 281

ChaLearn multi-modal gesture recognition 2013

Escalera, S., Gonzàlez, J., Baró, X., Reyes, M., Guyon, I., Athitsos, V., Escalante, H. J., Sigal, L., Argyros, A., Sminchisescu, C., et al. (2013) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2522848.2532597 · Cited by 51