The DOI matches its Crossref record; 15 of the 16 references it cites were found in a registry or an index.
Real time hand pose estimation using depth sensors
Abstract
This paper describes a depth image based real-time skeleton fitting algorithm for the hand, using an object recognition by parts approach, and the use of this hand modeler in an American Sign Language (ASL) digit recognition application. In particular, we created a realistic 3D hand model that represents the hand with 21 different parts. Random decision forests (RDF) are trained on synthetic depth images generated by animating the hand model, which are then used to perform per pixel classification and assign each pixel to a hand part. The classification results are fed into a local mode finding algorithm to estimate the joint locations for the hand skeleton. The system can process depth images retrieved from Kinect in real-time at 30 fps. As an application of the system, we also describe a support vector machine (SVM) based recognition module for the ten digits of ASL based on our method, which attains a recognition rate of 99.9% on live depth images in real-time1.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Mean shift: a robust approach toward feature space analysis
Comaniciu, D., & Meer, P. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.1000236 · Cited by 11,523
CONDENSATION—Conditional Density Propagation for Visual Tracking
Isard, M., & Blake, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008078328650 · Cited by 4,886
Real-time human pose recognition in parts from single depth images
Shotton, J., Fitzgibbon, A. W., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., & Blake, A. (2011) · 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.2011.5995316 · Cited by 3,545
Vision-based hand pose estimation: A review
Erol, A., Bebis, G. N., Nicolescu, M., Boyle, R. D., & Twombly, X. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.10.012 · Cited by 866
Estimating 3D hand pose from a cluttered image
Athitsos, V., & Sclaroff, S. (2003) · 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.2003.1211500 · Cited by 387
Model-Based 3D Hand Pose Estimation from Monocular Video
Gorce, M. D. L., Fleet, D. J., & Nikos Paragios, A. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.33 · Cited by 271
Model-based 3D tracking of an articulated hand
Stenger, B., Mendonça, P. R. S., & Cipolla, R. (2005) · 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.2001.990976 · Cited by 266
Hand gesture recognition using a neural network shape fitting technique
Stergiopoulou, E., & Papamarkos, N. (2009) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2009.03.008 · Cited by 264
Hand gesture recognition using depth data
Liu, X., & Fujimura, K. (2004) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2004.1301587 · Cited by 205
Research works printed but not linked: 4
- welch, 1995, An introduction to the Kalman filter
- tipping, 2001, Sparse Bayesian Learning and the Relevance Vector Machine
- oikonomidis, 2011, Markerless and efficient 26-DOF hand pose recovery, Proceedings of the 10th Asian Conference on Computer Vision-Volume Part, 3, 744
- redmond, 0, Kinect for Xbox, 360
Cited by
Sorted by citations
Enhanced Computer Vision With Microsoft Kinect Sensor: A Review
Han, J., Shao, L., Xu, D., & Shotton, J. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2265378 · Cited by 1,360
GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB
Mueller, F., Bernard, F., Sotnychenko, O., Mehta, D., Sridhar, S., Casas, D., & Theobalt, C. (2018) · 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.2018.00013 · Cited by 516
Ohn-Bar, E., & Trivedi, M. M. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2337331 · Cited by 454
ModDrop: Adaptive Multi-Modal Gesture Recognition
Neverova, N., Wolf, C., Taylor, G., & Nebout, F. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2461544 · Cited by 420
Hand gesture recognition with depth images: A review
Suarez, J., & Murphy, R. R. (2012) · IEEE RO-MAN · doi:10.1109/roman.2012.6343787 · Cited by 394
Survey on 3D Hand Gesture Recognition
Cheng, H., Yang, L., & Liu, Z. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2469551 · Cited by 346
Hand Pose Estimation and Hand Shape Classification Using Multi-layered Randomized Decision Forests
Keskin, C., Kıraç, F., Kara, Y. E., & Akarun, L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33783-3_61 · Cited by 288
Self-citationTraining a Feedback Loop for Hand Pose Estimation
Oberweger, M., Wohlhart, P., & Lepetit, V. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.379 · Cited by 275
Real-Time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor
Mueller, F., Mehta, D., Sotnychenko, O., Sridhar, S., Casas, D., & Theobalt, C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.131 · Cited by 273
DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation
Oberweger, M., & Lepetit, V. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.75 · Cited by 269
Review of constraints on vision‐based gesture recognition for human–computer interaction
Chakraborty, B. K., Sarma, D., Bhuyan, M. K., & MacDorman, K. F. (2017) · IET Computer Vision · doi:10.1049/iet-cvi.2017.0052 · Cited by 244
American Sign Language alphabet recognition using Microsoft Kinect
Dong, C., Leu, M. C., & Yin, Z. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301347 · Cited by 243
Multi-scale Deep Learning for Gesture Detection and Localization
Neverova, N., Wolf, C., Taylor, G. W., & Nebout, F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16178-5_33 · Cited by 246
Fast and robust hand tracking using detection-guided optimization
Sridhar, S., Mueller, F., Oulasvirta, A., & Theobalt, C. (2015) · 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.2015.7298941 · Cited by 233
Qin, L., & Isa, N. A. M. (2023) · IEEE Access · doi:10.1109/access.2023.3245093 · Cited by 258
Hand gesture recognition with jointly calibrated Leap Motion and depth sensor
Marin, G., Dominio, F., & Zanuttigh, P. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-2451-6 · Cited by 220
Lachat, E., Macher, H., Landes, T., & Grussenmeyer, P. (2015) · Remote Sensing · doi:10.3390/rs71013070 · Cited by 212
Fusion of Inertial and Depth Sensor Data for Robust Hand Gesture Recognition
Liu, K., Chen, C., Jafari, R., & Kehtarnavaz, N. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2306094 · Cited by 163
Real-Time Sign Language Recognition Using a Consumer Depth Camera
Kuznetsova, A., Leal-Taixé, L., & Rosenhahn, B. (2013) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.18 · Cited by 157
A novel finger and hand pose estimation technique for real-time hand gesture recognition
Zhou, Y.-M., Jiang, G., & Lin, Y. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2015.07.014 · Cited by 143
Wearable Sensor-Based Sign Language Recognition: A Comprehensive Review
Kudrinko, K., Flavin, E., Zhu, X., & Li, Q. (2020) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.3019769 · Cited by 148
Gesture Recognition Using mm-Wave Sensor for Human-Car Interface
Smith, K., Csech, C., Murdoch, D., & Shaker, G. S. A. (2018) · IEEE Sensors Letters · doi:10.1109/lsens.2018.2810093 · Cited by 140
Contour Model-Based Hand-Gesture Recognition Using the Kinect Sensor
Yao, Y., & Fu, Y. (2014) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2014.2302538 · Cited by 130
Real time action recognition using histograms of depth gradients and random decision forests
Rahmani, H., Mahmood, A., Huynh, D. Q., & Mian, A. S. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6836044 · Cited by 116
Combining multiple depth-based descriptors for hand gesture recognition
Dominio, F., Donadeo, M., & Zanuttigh, P. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.10.010 · Cited by 104
Parsing the Hand in Depth Images
Liang, H., Yuan, J., & Thalmann, D. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2306177 · Cited by 99
Real-Time Hand Tracking Under Occlusion from an Egocentric RGB-D Sensor
Mueller, F., Mehta, D., Sotnychenko, O., Sridhar, S., Casas, D., & Theobalt, C. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.82 · Cited by 95
An Efficient Hand Gesture Recognition System Based on Deep CNN
Chung, H., Chung, Y.-L., & Tsai, W.-F. (2019) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2019.8755038 · Cited by 93
Real-Time American Sign Language Recognition System Using Surface EMG Signal
Savur, C., & Sahin, F. (2015) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2015.212 · Cited by 87
Fused features mining for depth-based hand gesture recognition to classify blind human communication
Jadooki, S., Mohamad, D., Saba, T., Almazyad, A. S., & Rehman, A. U. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2244-5 · Cited by 85
Applying Hand Gesture Recognition for User Guide Application Using MediaPipe
Indriani, I., Harris, M., & Agoes, A. S. (2021) · Advances in engineering research/Advances in Engineering Research · doi:10.2991/aer.k.211106.017 · Cited by 89
Generalized Feedback Loop for Joint Hand-Object Pose Estimation
Oberweger, M., Wohlhart, P., & Lepetit, V. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2907951 · Cited by 83
American Sign Language Recognition system by using surface EMG signal
Savur, C., & Sahin, F. (2016) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2016.7844675 · Cited by 75
Advancements in Sign Language Recognition: A Comprehensive Review and Future Prospects
Abdullah, B. A. A., Amoudi, G., & Alghamdi, H. S. (2024) · IEEE Access · doi:10.1109/access.2024.3457692 · Cited by 65
Latent Support Vector Machine Modeling for Sign Language Recognition with Kinect
Sun, C., Zhang, T., & Xu, C. (2015) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2629481 · Cited by 62
Recognition of Hand Gestures Observed by Depth Cameras
Kapuściński, T., Oszust, M., Wysocki, M., & Warchoł, D. (2015) · International Journal of Advanced Robotic Systems · doi:10.5772/60091 · Cited by 59
Hand pose estimation and tracking in real and virtual interaction:A review
Ahmad, A., Migniot, C., & Dipandà, A. (2019) · Image and Vision Computing · doi:10.1016/j.imavis.2019.06.003 · Cited by 59
Real-time robust vision-based hand gesture recognition using stereo images
Liu, K., & Kehtarnavaz, N. (2013) · Journal of Real-Time Image Processing · doi:10.1007/s11554-013-0333-6 · Cited by 58
Hernández-Vela, A., Bautista, M. Á., Perez-Sala, X., Ponce-López, V., Escalera, S., Baró, X., Pujol, O., & Ángulo, C. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.09.009 · Cited by 57
DART: dense articulated real-time tracking with consumer depth cameras
Schmidt, T., Newcombe, R., & Fox, D. (2015) · Autonomous Robots · doi:10.1007/s10514-015-9462-z · Cited by 57
Evolutionary Quasi-Random Search for Hand Articulations Tracking
Oikonomidis, I., Lourakis, M., & Argyros, A. (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.2014.437 · Cited by 55
3D Gestural Interaction: The State of the Field
LaViola, J. J. (2013) · ISRN Artificial Intelligence · doi:10.1155/2013/514641 · Cited by 55
Polish sign language words recognition with Kinect
Oszust, M., & Wysocki, M. (2013) · Conference on Human System Interactions · doi:10.1109/hsi.2013.6577826 · Cited by 54
Kim, D., Lee, S., Park, D., Piao, C., Park, J., Ahn, Y., Cho, K., Shin, J., Song, S. M., Kim, S., et al. (2020) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2020.3021246 · Cited by 54
Vision Based Hand Gesture Recognition Using 3D Shape Context
Zhu, C., Yang, J., Shao, Z., & Liu, C. (2019) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2019.1911534 · Cited by 51
A robust method of detecting hand gestures using depth sensors
Wen, Y., Hu, C., Yu, G., & Wang, C. (2012) · IEEE International Workshop on Haptic Audio visual Environments and Games · doi:10.1109/have.2012.6374441 · Cited by 50
Vision-Based Pose Estimation for Robot-Mediated Hand Telerehabilitation
Farulla, G. A., Pianu, D., Cempini, M., Cortese, M., Russo, L. O., Indaco, M., Nerino, R., Chimienti, A., Oddo, C. M., & Vitiello, N. (2016) · Sensors · doi:10.3390/s16020208 · Cited by 50
Prediction of Manipulation Actions
Fermuller, C. M., Wang, F., Yang, Y., Zampogiannis, K., Zhang, Y., Barranco, F., & Pfeiffer, M. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-0992-z · Cited by 49
Video-based Chinese sign language recognition using convolutional neural network
Yang, S., & Zhu, Q. (2017) · IEEE International Conference on Communication Software and Networks · doi:10.1109/iccsn.2017.8230247 · Cited by 39
Randomized decision forests for static and dynamic hand shape classification
Keskin, C., Kıraç, F., Kara, Y. E., & Akarun, L. (2012) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2012.6239183 · Cited by 46
Self-citationA Multisensor Technique for Gesture Recognition Through Intelligent Skeletal Pose Analysis
Rossol, N., Cheng, I., & Basu, A. (2015) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2015.2467212 · Cited by 45
Hand gesture recognition with depth data
Dominio, F., Donadeo, M., Marin, G., Zanuttigh, P., & Cortelazzo, G. M. (2013) · doi:10.1145/2510650.2510651 · Cited by 44
Depth-Projection-Map-Based Bag of Contour Fragments for Robust Hand Gesture Recognition
Feng, B., He, F., Wang, X., Wu, Y., Wang, H., Yi, S., & Liu, W. (2016) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2016.2616278 · Cited by 44
An Efficient Human Computer Interaction through Hand Gesture Using Deep Convolutional Neural Network
Islam, M. M., Islam, M. R., & Islam, M. S. (2020) · SN Computer Science · doi:10.1007/s42979-020-00223-x · Cited by 43
Hand Shape and 3D Pose Estimation Using Depth Data from a Single Cluttered Frame
Doliotis, P., Athitsos, V., Kosmopoulos, D., & Perantonis, S. J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33179-4_15 · Cited by 40
Multi-touchless: Real-time fingertip detection and tracking using geodesic maxima
Krejov, P., & Bowden, R. (2013) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2013.6553778 · Cited by 38
An Accurate Algorithm for the Identification of Fingertips Using an RGB-D Camera
Maisto, M., Panella, M., Liparulo, L., & Proietti, A. (2013) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2013.2256830 · Cited by 37
Metaphoric Hand Gestures for Orientation-Aware VR Object Manipulation With an Egocentric Viewpoint
Jang, Y., Jeon, I., Kim, T., & Woo, W. (2016) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2016.2611824 · Cited by 35
Recognition of signed expressions observed by Kinect Sensor
Oszust, M., & Wysocki, M. (2013) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2013.6636643 · Cited by 33
Hand Segmentation with Structured Convolutional Learning
Neverova, N., Wolf, C., Taylor, G. W., & Nebout, F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16811-1_45 · Cited by 32
Resolving Ambiguous Hand Pose Predictions by Exploiting Part Correlations
Liang, H., Yuan, J., & Thalmann, D. (2014) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2014.2363750 · Cited by 31
Guided optimisation through classification and regression for hand pose estimation
Krejov, P., Gilbert, A., & Bowden, R. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.11.005 · Cited by 31
Hierarchical particle filtering for 3D hand tracking
Makris, A., Kyriazis, N., & Argyros, A. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301343 · Cited by 30
LIBRAS Sign Language Hand Configuration Recognition Based on 3D Meshes
Porfirio, A., Wiggers, K. L., Oliveira, L. S., & Weingaertner, D. (2013) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2013.274 · Cited by 29
Real-Time Hand Pose Estimation from RGB-D Sensor
Yao, Y., & Fu, Y. (2012) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2012.48 · Cited by 29
An introduction to 3D gestural interfaces
LaViola, J. J. (2014) · ACM SIGGRAPH Courses · doi:10.1145/2614028.2615424 · Cited by 27
Action Classification with Locality-Constrained Linear Coding
Rahmani, H., Mahmood, A., Huynh, D. Q., & Mian, A. S. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.604 · Cited by 26
Learning a deep network with spherical part model for 3D hand pose estimation
Chen, T.-Y., Ting, P.-W., Wu, M.-Y., & Fu, L. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.02.029 · Cited by 26
Real-time hand posture recognition based on hand dominant line using kinect
Wang, Y., & Yang, R. (2013) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew.2013.6618237 · Cited by 26
Warchoł, D., Kapuściński, T., & Wysocki, M. (2019) · Sensors · doi:10.3390/s19051078 · Cited by 23
Hand gesture recognition via image processing techniques and deep CNN
Chung, Y.-L., Chung, H., & Tsai, W.-F. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-200385 · Cited by 23
Human Behavior Understanding for Inducing Behavioral Change: Application Perspectives
Salah, A. A., Lepri, B., Pianesi, F., & Pentland, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25446-8_1 · Cited by 22
Gesture-based system for next generation natural and intuitive interfaces
Huang, J., Jaiswal, P., & Rai, R. (2018) · Artificial intelligence for engineering design analysis and manufacturing · doi:10.1017/s0890060418000045 · Cited by 22
A Kinect-based handwritten digit recognition for TV remote controller
Chu, T.-T., & Su, C. (2012) · International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) · doi:10.1109/ispacs.2012.6473522 · Cited by 20
Enhanced multi-objective particle swarm optimisation for estimating hand postures
Saremi, S., Mirjalili, S., Lewis, A., Liew, A. W., & Dong, J. S. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.05.043 · Cited by 20
Ariesta, M. C., Wiryana, F., Suharjito, S., & Zahra, A. (2018) · Indonesian Association for Pattern Recognition International Conference (INAPR) · doi:10.1109/inapr.2018.8627016 · Cited by 24
Hand pose estimation with multi-scale network
Hu, Z., Hu, Y., Wu, B., Liu, J., Han, D., & Kurfess, T. R. (2017) · Applied Intelligence · doi:10.1007/s10489-017-1092-z · Cited by 19
On the Fusion of RGB and Depth Information for Hand Pose Estimation
Kazakos, E., Nikou, C., & Kakadiaris, I. A. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451022 · Cited by 18
3D Hand Pose Estimation on Conventional Capacitive Touchscreens
Choi, F., Mayer, S., & Harrison, C. (2021) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/3447526.3472045 · Cited by 18
Hand Gesture Recognition towards Enhancing Accessibility
Cardoso, T., Delgado, J. R. F., & Barata, J. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.09.287 · Cited by 18
An Ultra-Efficient Approach for High-Resolution MIMO Radar Imaging of Human Hand Poses
Bräunig, J., Wirth, V., Kammel, C., Schüßler, C., Ullmann, I., Stamminger, M., & Vossiek, M. (2023) · IEEE Transactions on Radar Systems · doi:10.1109/trs.2023.3309574 · Cited by 17
Eliciting Pen-Holding Postures for General Input with Suitability for EMG Armband Detection
Matulic, F., Vogel, B., Kimura, N., & Vogel, D. (2019) · ACM International Conference on Interactive Surfaces and Spaces (ISS) · doi:10.1145/3343055.3359720 · Cited by 17
Automatic multimodal descriptors of rhythmic body movement
Mahmoud, M., Morency, L., & Robinson, P. (2013) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2522848.2522895 · Cited by 17
Human Behavior Analysis from Depth Maps
Escalera, S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31567-1_28 · Cited by 16
Combining Leap Motion with Unity for Virtual Glove Puppets
Lin, C. Y., Yang, Z.-H., Zhou, H.-W., Yang, T.-N., Chen, H.-N., & Shih, T. K. (2018) · IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) · doi:10.1109/aivr.2018.00059 · Cited by 16
Vision-based hand grasping posture recognition in drinking activity
Chua, J.-L., Chang, Y. C., Jaward, M., Parkkinen, J., & Wong, K. (2014) · International Symposium on Intelligent Signal Processing and Communication Systems · doi:10.1109/ispacs.2014.7024449 · Cited by 15
An advanced interaction framework for augmented reality based exposure treatment
Corbett‐Davies, S., Duenser, A., Green, R., & Clark, A. (2013) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr.2013.6549351 · Cited by 15
Tracking Hands in Interaction with Objects: A Review
Ahmad, A., Migniot, C., & Dipandà, A. (2017) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2017.66 · Cited by 14
Finger detection and hand posture recognition based on depth information
Poularakis, S., & Katsavounidis, I. X. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854419 · Cited by 14
Towards Deep Learning based Hand Keypoints Detection for Rapid Sequential Movements from RGB Images
Gattupalli, S., Babu, A. R., Brady, J., Makedon, F. S., & Athitsos, V. (2018) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3197768.3201538 · Cited by 13
Feature Extraction Methods in Sign Language Recognition System: A Literature Review
Suharjito, S., Wiryana, F., Kusuma, G. P., & Zahra, A. (2018) · Indonesian Association for Pattern Recognition International Conference (INAPR) · doi:10.1109/inapr.2018.8626857 · Cited by 13
Real-time Hand Gesture Recognition Based on Deep Learning in Complex Environments
Wu, W., Shi, M., Wu, T., Zhao, D., Zhang, S., & Li, J. (2019) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2019.8833328 · Cited by 13
Real-Time Hand Pose Estimation Using Classifiers
Półrola, M., & Wojciechowski, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33564-8_69 · Cited by 12
Simple very deep convolutional network for robust hand pose regression from a single depth image
Fan, Q., Shen, X., Hu, Y. S., & Yu, C. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.10.019 · Cited by 11
Ohn-Bar, E., & Trivedi, M. M. (2015) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2015.7225790 · Cited by 10
Madadi, M., Escalera, S., Baró, X., & Gonzàlez, J. (2021) · IET Computer Vision · doi:10.1049/cvi2.12064 · Cited by 9
Olsen, M. D., Herskind, A., Nielsen, J. B., & Paulsen, R. R. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.377 · Cited by 9
Control and Guidance of Low-Cost Robots via Gesture Perception for Monitoring Activities in the Home
Sempere, Á. P., Serna-Leon, A., Gil, P., Méndez, S. T. P., & Torres, F. (2015) · Sensors · doi:10.3390/s151229853 · Cited by 9
CrossFuNet: RGB and Depth Cross-Fusion Network for Hand Pose Estimation
Sun, X., Wang, B., Huang, L., Zhang, Q., Zhu, S., & Ma, Y. (2021) · Sensors · doi:10.3390/s21186095 · Cited by 9
Physically interactive tabletop augmented reality using the Kinect
Corbett‐Davies, S., Green, R., & Clark, A. (2012) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1145/2425836.2425880 · Cited by 8
Metadata
- Authors
- Keskin, C., Kirac, F., Kara, Y. E., & Akarun, L.
- Affiliations
- Boğaziçi University (TR)
- ORCID
- Akarun, Lale
- Venue
- 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
- Publisher
- IEEE
- Published
- 2011-11
- Pages
- 1228-1234
- ISSN
- 2473-9936 (ISSN-L)
- OpenAlex
- W2407277412
- Cited by
- 190
- References
- 16
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 2 · 2023: 8 · 2022: 2 · 2021: 6 · 2020: 10 · 2019: 14 · 2018: 18 · 2017: 12 · 2016: 18 · 2015: 28 · 2014: 26 · 2013: 22 · 2012: 14
- FWCI
- 15.02
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Computer Vision Workshops (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Keywords
- hand pose estimation, depth sensors, 3D hand model, random forest, American Sign Language, real-time processing, Kinect sensor
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
