Abstract
We present Machete, a straightforward segmenter one can use to isolate custom gestures in continuous input. Machete uses traditional continuous dynamic programming with a novel dissimilarity measure to align incoming data with gesture class templates in real time. Advantages of Machete over alternative techniques is that our segmenter is computationally efficient, accurate, device-agnostic, and works with a single training sample. We demonstrate Machete’s effectiveness through an extensive evaluation using four new high-activity datasets that combine puppeteering, direct manipulation, and gestures. We find that Machete outperforms three alternative techniques in segmentation accuracy and latency, making Machete the most performant segmenter. We further show that when combined with a custom gesture recognizer, Machete is the only option that achieves both high recognition accuracy and low latency in a video game application.
- Jonathan Alon, Vassilis Athitsos, Quan Yuan, and Stan Sclaroff. 2009. A unified framework for gesture recognition and spatiotemporal gesture segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 9 (2009), 1685--1699.Google ScholarDigital Library
- Lisa Anthony, YooJin Kim, and Leah Findlater. 2013. Analyzing user-generated YouTube videos to understand touchscreen use by people with motor impairments. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1223--1232.Google ScholarDigital Library
- Lisa Anthony and Jacob O. Wobbrock. 2010. A lightweight multistroke recognizer for user interface prototypes. In Proceedings of the 2010 Graphics Interface (GI’10). Canadian Information Processing Society, Toronto, Ontario, Canada, 245--252.Google Scholar
- Lisa Anthony and Jacob O. Wobbrock. 2012. $N-protractor: A fast and accurate multistroke recognizer. In Proceedings of the 2012 Graphics Interface (GI’12). Canadian Information Processing Society, Toronto, Ontario, Canada, 117--120.Google Scholar
- Robert Arn, Pradyumna Narayana, Bruce Draper, Tegan Emerson, Michael Kirby, and Chris Peterson. 2018. Motion segmentation via generalized curvatures. IEEE Transactions on Pattern Analysis and Machine Intelligence 41, 12 (2018), 2919–2932.Google ScholarCross Ref
- Gustavo E. A. P. A. Batista, Xiaoyue Wang, and Eamonn J. Keogh. 2011. A complexity-invariant distance measure for time series. In Proceedings of the 11th SIAM International Conference on Data Mining. 699--710.Google Scholar
- Rachel Blagojevic, Samuel Hsiao-Heng Chang, and Beryl Plimmer. 2010. The power of automatic feature selection: Rubine on steroids. In Proceedings of the 7th Sketch-Based Interfaces and Modeling Symposium. Eurographics Association, 79--86.Google Scholar
- Aaron Bobick and James Davis. 1996. Real-time recognition of activity using temporal templates. In Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision. IEEE, 39--42.Google ScholarCross Ref
- Aaron F. Bobick and Andrew D. Wilson. 1995. A state-based technique for the summarization and recognition of gesture. In Proceedings of the 5th International Conference on Computer Vision. IEEE, 382--388.Google Scholar
- Lee W. Campbell and Aaron F. Bobick. 1995. Recognition of human body motion using phase space constraints. In Proceedings of the 5th International Conference on Computer Vision. IEEE, 624--630.Google Scholar
- F. M. Caputo, S. Burato, G. Pavan, T. Voillemin, H. Wannous, J. P. Vandeborre, M. Maghoumi, E. M. Taranta, A. Razmjoo, J. J. LaViola Jr, F. Manganaro, S. Pini, G. Borghi, R. Vezzani, R. Cucchiara, H. Nguyen, M. T. Tran, and A. Giachetti. 2019. Online gesture recognition. In Proceedings of the Eurographics Workshop on 3D Object Retrieval.Google Scholar
- Fabio Marco Caputo, Pietro Prebianca, Alessandro Carcangiu, Lucio D. Spano, and Andrea Giachetti. 2017. A 3 cent recognizer: Simple and effective retrieval and classification of mid-air gestures from single 3D traces. In Proceedings of the Conference on Smart Tools and Apps for Graphics. Eurographics Association.Google Scholar
- Géry Casiez, Nicolas Roussel, and Daniel Vogel. 2012. 1€ filter: A simple speed-based low-pass filter for noisy input in interactive systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2527--2530.Google ScholarDigital Library
- Edwin Chan, Teddy Seyed, Wolfgang Stuerzlinger, Xing-Dong Yang, and Frank Maurer. 2016. User elicitation on single-hand microgestures. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 3403--3414.Google ScholarDigital Library
- Salman Cheema, Michael Hoffman, and Joseph J. LaViola Jr. 2013. 3D gesture classification with linear acceleration and angular velocity sensing devices for video games. Entertainment Computing 4, 1 (2013), 11--24.Google ScholarCross Ref
- Jessie Y. C. Chen and Jennifer E. Thropp. 2007. Review of low frame rate effects on human performance. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 37, 6 (2007), 1063--1076.Google ScholarDigital Library
- Yineng Chen, Xiaojun Su, Feng Tian, Jin Huang, Xiaolong Luke Zhang, Guozhong Dai, and Hongan Wang. 2016. Pactolus: A method for mid-air gesture segmentation within EMG. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 1760--1765.Google ScholarDigital Library
- Kyunghyun Cho, Bart van Merriënboer, Caglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder–decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’18). Association for Computational Linguistics, Doha, Qatar, 1724--1734. Retrieved from http://www.aclweb.org/anthology/D14-1179.Google ScholarCross Ref
- Mark Claypool, Kajal Claypool, and Feissal Damaa. 2006. The effects of frame rate and resolution on users playing first person shooter games. In Multimedia Computing and Networking 2006. Vol. 6071. International Society for Optics and Photonics, 607101.Google ScholarCross Ref
- M. Devanne, H. Wannous, S. Berretti, P. Pala, M. Daoudi, and A. Del Bimbo. 2015. 3-D human action recognition by shape analysis of motion trajectories on riemannian manifold. IEEE Transactions on Cybernetics 45, 7 (July 2015), 1340--1352.Google ScholarCross Ref
- David H. Douglas and Thomas K. Peucker. 1973. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: The International Journal for Geographic Information and Geovisualization 10, 2 (1973), 112--122.Google ScholarCross Ref
- Abdallah El Ali, Johan Kildal, and Vuokko Lantz. 2012. Fishing or a Z? Investigating the effects of error on mimetic and alphabet device-based gesture interaction. In Proceedings of the 14th ACM International Conference on Multimodal Interaction. 93--100.Google ScholarDigital Library
- Chris Ellis, Syed Zain Masood, Marshall F. Tappen, Joseph J. Laviola, Jr., and Rahul Sukthankar. 2013. Exploring the trade-off between accuracy and observational latency in action recognition. International Journal of Computer Vision 101, 3 (Feb. 2013), 420--436.Google ScholarDigital Library
- Sergio Escalera, Vassilis Athitsos, and Isabelle Guyon. 2017. Challenges in multi-modal gesture recognition. In Gesture Recognition. Springer, 1--60.Google Scholar
- Milton Friedman. 1937. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association 32, 200 (1937), 675--701.Google ScholarCross Ref
- Vittorio Fuccella and Gennaro Costagliola. 2015. Unistroke gesture recognition through polyline approximation and alignment. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, 3351--3354.Google ScholarDigital Library
- Wayne D. Gray and Deborah A. Boehm-Davis. 2000. Milliseconds matter: An introduction to microstrategies and to their use in describing and predicting interactive behavior. Journal of Experimental Psychology: Applied 6, 4 (2000), 322.Google ScholarCross Ref
- James Herold and Thomas F. Stahovich. 2012. The 1c| recognizer: A fast, accurate, and easy-to-implement handwritten gesture recognition technique. In Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling. Eurographics Association, 39--46.Google Scholar
- Sture Holm. 1979. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 2 (1979), 65--70.Google Scholar
- J. Hu, W. Zheng, J. Lai, and J. Zhang. 2017. Jointly learning heterogeneous features for RGB-D activity recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 39, 11 (Nov 2017), 2186--2200. DOI:https://doi.org/10.1109/TPAMI.2016.2640292Google ScholarDigital Library
- Jian-Fang Hu, Wei-Shi Zheng, Lianyang Ma, Gang Wang, and Jianhuang Lai. 2016. Real-time RGB-D activity prediction by soft regression. In Proceedings of the European Conference on Computer Vision (ECCV’16), Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling (Eds.). Springer International Publishing, Cham, 280--296.Google ScholarCross Ref
- Yoonho Hwang, Bohyung Han, and Hee-Kap Ahn. 2012. A fast nearest neighbor search algorithm by nonlinear embedding. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’12). 3053--3060.Google ScholarDigital Library
- Yoonho Hwang and Hee kap Ahn. 2011. Convergent bounds on the Euclidean distance. In Proceedings of the Advances in Neural Information Processing Systems, J. Shawe-taylor, R. S. Zemel, P. Bartlett, F. C. N. Pereira, and K. Q. Weinberger (Eds.). 388--396.Google Scholar
- Holger Junker, Oliver Amft, Paul Lukowicz, and Gerhard Tröster. 2008. Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recognition 41, 6 (2008), 2010--2024.Google ScholarDigital Library
- Kanav Kahol, Priyamvada Tripathi, and Sethuraman Panchanathan. 2004. Automated gesture segmentation from dance sequences. In Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition. IEEE, 883--888.Google ScholarCross Ref
- Hyun Kang, Chang Woo Lee, and Keechul Jung. 2004. Recognition-based gesture spotting in video games. Pattern Recognition Letters 25, 15 (2004), 1701--1714.Google ScholarDigital Library
- Maria Karam and M. C. Schraefel. 2006. Investigating user tolerance for errors in vision-enabled gesture-based interactions. In Proceedings of the Working Conference on Advanced Visual Interfaces. 225--232.Google Scholar
- Eamonn Keogh, Selina Chu, David Hart, and Michael Pazzani. 2004. Segmenting time series: A survey and novel approach. In Data Mining in Time Series Databases. World Scientific, 1--21.Google Scholar
- Eamonn Keogh and Chotirat Ann Ratanamahatana. 2005. Exact indexing of dynamic time warping. Knowledge and Information Systems 7, 3 (2005), 358--386.Google ScholarDigital Library
- Jungsoo Kim, Jiasheng He, Kent Lyons, and Thad Starner. 2007. The gesture watch: A wireless contact-free gesture based wrist interface. In Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers. IEEE, 15--22.Google ScholarDigital Library
- Sven Kratz and Michael Rohs. 2010. The $3 recognizer: Simple 3D gesture recognition on mobile devices. In Proceedings of the 15th International Conference on Intelligent User Interfaces. ACM, 419--420.Google ScholarDigital Library
- Sven Kratz and Michael Rohs. 2011. Protractor3D: A closed-form solution to rotation-invariant 3D gestures. In Proceedings of the 16th International Conference on Intelligent User Interfaces. ACM, 371--374.Google ScholarDigital Library
- Per Ola Kristensson, Thomas Nicholson, and Aaron Quigley. 2012. Continuous recognition of one-handed and two-handed gestures using 3D full-body motion tracking sensors. In Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces. ACM, 89--92.Google ScholarDigital Library
- Eyal Krupka, Kfir Karmon, Noam Bloom, Daniel Freedman, Ilya Gurvich, Aviv Hurvitz, Ido Leichter, Yoni Smolin, Yuval Tzairi, Alon Vinnikov, and Aharon Bar Hillel. 2017. Toward realistic hands gesture Interface: Keeping it simple for developers and machines. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 1887--1898.Google ScholarDigital Library
- Joseph J. LaViola. 2003. Double exponential smoothing: An alternative to Kalman filter-based predictive tracking. In Proceedings of the Workshop on Virtual Environments 2003. ACM, 199--206.Google ScholarDigital Library
- Yang Li. 2010. Protractor: A fast and accurate gesture recognizer. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’10). ACM, New York, NY, 2169--2172.Google ScholarDigital Library
- Jiayang Liu, Lin Zhong, Jehan Wickramasuriya, and Venu Vasudevan. 2009. uWave: Accelerometer-based personalized gesture recognition and its applications. Pervasive and Mobile Computing 5, 6 (2009), 657--675.Google ScholarDigital Library
- Granit Luzhnica, Jorg Simon, Elisabeth Lex, and Viktoria Pammer. 2016. A sliding window approach to natural hand gesture recognition using a custom data glove. In Proceedings of the 2016 IEEE Symposium on 3D User Interfaces. IEEE, 81--90.Google ScholarCross Ref
- Mehran Maghoumi and Joseph J. LaViola. 2019. DeepGRU: Deep gesture recognition utility. In Proceedings of the International Symposium on Visual Computing. 16--31.Google Scholar
- Thomas M. Mitchell. 1997. Machine Learning (1 ed.). McGraw-Hill, Inc., New York, NY.Google ScholarDigital Library
- Meredith Ringel Morris, Jacob O. Wobbrock, and Andrew D. Wilson. 2010. Understanding users’ preferences for surface gestures. In Proceedings of the 2010 Graphics Interface. Canadian Information Processing Society, 261--268.Google ScholarDigital Library
- Miguel A. Nacenta, Yemliha Kamber, Yizhou Qiang, and Per Ola Kristensson. 2013. Memorability of pre-designed and user-defined gesture sets. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1099--1108.Google ScholarDigital Library
- Pedro Neto, Dário Pereira, J. Norberto Pires, and A. Paulo Moreira. 2013. Real-time and continuous hand gesture spotting: An approach based on artificial neural networks. In Proceedings of the 2013 IEEE International Conference on Robotics and Automation. IEEE, 178--183.Google Scholar
- Yuan Niu and Hao Chen. 2011. Gesture authentication with touch input for mobile devices. In Proceedings of the International Conference on Security and Privacy in Mobile Information and Communication Systems. Springer, 13--24.Google Scholar
- Juliet Norton, Chadwick A. Wingrave, and Joseph J. LaViola Jr. 2010. Exploring strategies and guidelines for developing full body video game interfaces. In Proceedings of the 5th International Conference on the Foundations of Digital Games. ACM, 155--162.Google Scholar
- LLC Oculus VR. 2017. Best Practices. Thorlabs.Google Scholar
- Uran Oh and Leah Findlater. 2013. The challenges and potential of end-user gesture customization. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1129--1138.Google ScholarDigital Library
- Ryuichi Oka. 1998. Spotting method for classification of real world data. Computer Journal 41, 8 (1998), 559--565.Google ScholarCross Ref
- Corey Pittman, Pamela Wisniewski, Conner Brooks, and Joseph J. LaViola Jr. 2016. Multiwave: Doppler effect based gesture recognition in multiple dimensions. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 1729--1736.Google Scholar
- Thanawin Rakthanmanon, Bilson Campana, Abdullah Mueen, Gustavo Batista, Brandon Westover, Qiang Zhu, Jesin Zakaria, and Eamonn Keogh. 2012. Searching and mining trillions of time series subsequences under dynamic time warping. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 262--270.Google ScholarDigital Library
- Chotirat Ann Ratanamahatana and Eamonn Keogh. 2005. Three myths about dynamic time warping data mining. In Proceedings of the 2005 SIAM International Conference on Data Mining. SIAM, 506--510.Google ScholarCross Ref
- Dean Rubine. 1991. The Automatic Recognition of Gestures. Ph.D. Dissertation. Citeseer.Google Scholar
- Dean Rubine. 1991. Specifying gestures by example. SIGGRAPH Computer Graphics 25, 4 (July 1991), 329--337.Google ScholarDigital Library
- Hiroaki Sakoe and Seibi Chiba. 1978. Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing 26, 1 (1978), 43--49.Google ScholarCross Ref
- Yasushi Sakurai, Christos Faloutsos, and Masashi Yamamuro. 2007. Stream monitoring under the time warping distance. In Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering. IEEE, 1046--1055.Google ScholarCross Ref
- Junjie Shan and Srinivas Akella. 2014. 3D human action segmentation and recognition using pose kinetic energy. In Proceedings of the 2014 IEEE Workshop on Advanced Robotics and Its Social Impacts (ARSO’14). IEEE, 69--75.Google ScholarCross Ref
- Rim Slama, Hazem Wannous, Mohamed Daoudi, and Anuj Srivastava. 2015. Accurate 3D action recognition using learning on the Grassmann Manifold. Pattern Recognition 48, 2 (Feb. 2015), 556--567. DOI:https://doi.org/10.1016/j.patcog.2014.08.011Google ScholarDigital Library
- Steven W. Smith. 1997. The Scientist and Engineer’s Guide to Digital Signal Processing. California Technical Publishing, San Diego.Google ScholarDigital Library
- Yale Song, David Demirdjian, and Randall Davis. 2012. Continuous body and hand gesture recognition for natural human-computer interaction. ACM Transactions on Interactive Intelligent Systems 2, 1 (2012), 5.Google ScholarDigital Library
- Jingren Tang, Hong Cheng, Yang Zhao, and Hongliang Guo. 2018. Structured dynamic time warping for continuous hand trajectory gesture recognition. Pattern Recognition 80 (2018), 21--31. http://www.sciencedirect.com/science/article/pii/S0031320318300621.Google ScholarCross Ref
- Eugene M. Taranta II, Seng Lee Koh, Brian M. Williamson, Kevin P. Pfeil, Corey R. Pittman, and Joseph J. LaViola Jr. 2019. Pitch Pipe: An automatic low-pass filter calibration technique for pointing tasks. In Proceedings of the 45th Graphics Interface Conference on Proceedings of Graphics Interface 2019. Canadian Human-Computer Communications Society, 1--8.Google Scholar
- Eugene M. Taranta II, Mehran Maghoumi, Corey R. Pittman, and Joseph J. LaViola, Jr. 2016. A rapid prototyping approach to synthetic data generation for improved 2D gesture recognition. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST’16). ACM, New York, NY, 873--885.Google Scholar
- Eugene M. Taranta II, Corey R. Pittman, Jack P. Oakley, Mykola Maslych, Mehran Maghoumi, and Joseph J. LaViola Jr. 2020. Moving toward an ecologically valid data collection protocol for 2D gestures in video games. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1--11.Google Scholar
- Eugene M. Taranta II, Amirreza Samiei, Mehran Maghoumi, Pooya Khaloo, Corey R. Pittman, and Joseph J. LaViola Jr. 2017. Jackknife: A reliable recognizer with few samples and many modalities. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI’17). ACM, New York, NY, 5850--5861.Google Scholar
- Eugene M. Taranta II, Thaddeus K. Simons, Rahul Sukthankar, and Joseph J. Laviola Jr. 2015. Exploring the benefits of context in 3D gesture recognition for game-based virtual environments. ACM Transactions on Interactive Intelligent Systems 5, 1 (2015), 1.Google Scholar
- Eugene M. Taranta II, Andrés N. Vargas, and Joseph J. LaViola Jr. 2016. Streamlined and accurate gesture recognition with Penny Pincher. Computers 8 Graphics 55 (2016), 130--142. http://www.sciencedirect.com/science/article/pii/S0097849315001788.Google Scholar
- Radu-Daniel Vatavu. 2017. Improving gesture recognition accuracy on touch screens for users with low vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI’17). ACM, New York, NY, 4667--4679.Google ScholarDigital Library
- Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as point clouds: A $P recognizer for user interface prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (ICMI’12). ACM, New York, NY, 273--280.Google ScholarDigital Library
- Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $ Q: A super-quick, articulation-invariant stroke-gesture recognizer for low-resource devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, 23.Google Scholar
- Radu-Daniel Vatavu, Laurent Grisoni, and Stefan-Gheorghe Pentiuc. 2009. Multiscale detection of gesture patterns in continuous motion trajectories. In Proceedings of the International Gesture Workshop. Springer, 85--97.Google Scholar
- Radu-Daniel Vatavu, Daniel Vogel, Géry Casiez, and Laurent Grisoni. 2011. Estimating the perceived difficulty of pen gestures. In Proceedings of the 13th International Conference on Human-computer Interaction - Volume Part II (INTERACT’11). Springer, Berlin, 89--106.Google ScholarCross Ref
- Duc-Hoang Vo, Huu-Hung Huynh, Thanh-Nghia Nguyen, and Jean Meunier. 2016. Automatic hand gesture segmentation for recognition of Vietnamese sign language. In Proceedings of the 7th Symposium on Information and Communication Technology. ACM, 368--373.Google ScholarDigital Library
- Pei Wang, Chunfeng Yuan, Weiming Hu, Bing Li, and Yanning Zhang. 2016. Graph based skeleton motion representation and similarity measurement for action recognition. In Proceedings of the European Conference on Computer Vision. Springer, 370--385.Google ScholarCross Ref
- Tian-Shu Wang, Heung-Yeung Shum, Ying-Qing Xu, and Nan-Ning Zheng. 2001. Unsupervised analysis of human gestures. In Proceedings of the Pacific-Rim Conference on Multimedia. Springer, 174--181.Google ScholarCross Ref
- Séamas Weech, Sophie Kenny, and Michael Barnett-Cowan. 2019. Presence and cybersickness in virtual reality are negatively related: A review. Frontiers in Psychology 10 (2019), 158. https://www.frontiersin.org/article/10.3389/fpsyg.2019.00158.Google ScholarCross Ref
- Daniel Weinland, Remi Ronfard, and Edmond Boyer. 2011. A survey of vision-based methods for action representation, segmentation and recognition. Computer Vision and Image Understanding 115, 2 (Feb. 2011), 224--241.Google ScholarDigital Library
- J. Weng, C. Weng, and J. Yuan. 2017. Spatio-temporal Naive-Bayes nearest-neighbor (ST-NBNN) for skeleton-based action recognition. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 445--454. DOI:https://doi.org/10.1109/CVPR.2017.55Google ScholarCross Ref
- Jacob O. Wobbrock, Meredith Ringel Morris, and Andrew D. Wilson. 2009. User-defined gestures for surface computing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1083--1092.Google Scholar
- Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without libraries, toolkits or training: A $1 recognizer for user interface prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (UIST’07). ACM, New York, NY, 159--168.Google ScholarDigital Library
- Di Wu, Fan Zhu, and Ling Shao. 2012. One shot learning gesture recognition from RGBD images. In Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW’12). IEEE, 7--12.Google ScholarCross Ref
- L. Xia, C. Chen, and J. K. Aggarwal. 2012. View invariant human action recognition using histograms of 3D joints. In Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 20--27. DOI:https://doi.org/10.1109/CVPRW.2012.6239233Google Scholar
- Hee-Deok Yang, Stan Sclaroff, and Seong-Whan Lee. 2009. Sign language spotting with a threshold model based on conditional random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 7 (2009), 1264--1277.Google ScholarDigital Library
- Yina Ye and Petteri Nurmi. 2015. Gestimator: Shape and stroke similarity based gesture recognition. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction. ACM, 219--226.Google ScholarDigital Library
- Ying Yin and Randall Davis. 2013. Gesture spotting and recognition using salience detection and concatenated hidden Markov models. In Proceedings of the 15th ACM on International Conference on Multimodal Interaction. ACM, 489--494.Google ScholarDigital Library
- Hansong Zeng and Yi Zhao. 2011. Sensing movement: Microsensors for body motion measurement. Sensors 11, 1 (2011), 638--660.Google ScholarCross Ref
- Yaodong Zhang and James R. Glass. 2011. An inner-product lower-bound estimate for dynamic time warping. In Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’11). IEEE, 5660--5663.Google Scholar
Index Terms
- Machete: Easy, Efficient, and Precise Continuous Custom Gesture Segmentation
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