The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques
Abstract
This paper presents a novel and real-time system for interaction with an application or video game via hand gestures. Our system includes detecting and tracking bare hand in cluttered background using skin detection and hand posture contour comparison algorithm after face subtraction, recognizing hand gestures via bag-of-features and multiclass support vector machine (SVM) and building a grammar that generates gesture commands to control an application. In the training stage, after extracting the keypoints for every training image using the scale invariance feature transform (SIFT), a vector quantization technique will map keypoints from every training image into a unified dimensional histogram vector (bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multiclass SVM to build the training classifier. In the testing stage, for every frame captured from a webcam, the hand is detected using our algorithm, then, the keypoints are extracted for every small image that contains the detected hand gesture only and fed into the cluster model to map them into a bag-of-words vector, which is finally fed into the multiclass SVM training classifier to recognize the hand gesture.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955 · cited 6×
“speeded up robust features (SURF) [15].”
InfluentialRobust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557
Libsvm : A library for support vector machines
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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
Digital Image Processing Using MATLAB
González, R. C., Woods, R. E., & Eddins, S. L. (2003) · Cited by 7,079
A comparison of methods for multiclass support vector machines
Hsu, C.-W., & Lin, C.-J. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.991427 · Cited by 6,838
Information theory, inference, and learning algorithms
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Cited by
Soli: ubiquitous gesture sensing with millimeter wave radar
Lien, J., Gillian, N., Karagozler, M. E., Amihood, P., Schwesig, C., Olson, E., Raja, H., & Poupyrev, I. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925953 · Cited by 957
A review of hand gesture and sign language recognition techniques
Cheok, M. J., Omar, Z., & Jaward, M. H. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-017-0705-5 · Cited by 600
Hand gesture recognition with 3D convolutional neural networks
Molchanov, P., Gupta, S., Kim, K., & Kautz, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301342 · Cited by 543
Multi-class Open Set Recognition Using Probability of Inclusion
Jain, L. P., Scheirer, W. J., & Boult, T. E. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_26 · Cited by 340
Static and Dynamic Hand Gesture Recognition in Depth Data Using Dynamic Time Warping
Plouffe, G., & Cretu, A.-M. (2016) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2015.2498560 · Cited by 266
Metadata
- Authors
- Dardas, N. H., & Georganas, N. D.
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2011-11
- Volume
- 60
- Issue
- 11
- Pages
- 3592-3607
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W2119656522
- Cited by
- 481
- References
- 51
- FWCI
- 38.11
- Type
- journal article
- Language
- en
- Open access
- closed
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- Dardas, N. H., & Georganas, N. D. (2011). Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/tim.2011.2161140
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
