The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Hand Gesture Recognition in Real Time for Automotive Interfaces: A Multimodal Vision-Based Approach and Evaluations
Abstract
In this paper, we develop a vision-based system that employs a combined RGB and depth descriptor to classify hand gestures. The method is studied for a human-machine interface application in the car. Two interconnected modules are employed: one that detects a hand in the region of interaction and performs user classification, and another that performs gesture recognition. The feasibility of the system is demonstrated using a challenging RGBD hand gesture data set collected under settings of common illumination variation and occlusion.
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Cites
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
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
A survey on vision-based human action recognition
Poppe, R. (2010) · Image and Vision Computing · doi:10.1016/j.imavis.2009.11.014 · Cited by 2,242
Dense Trajectories and Motion Boundary Descriptors for Action Recognition
Wang, H., Kläser, A., Schmid, C., & Liu, C.-L. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-012-0594-8 · Cited by 1,669
Real time hand pose estimation using depth sensors
Keskin, C., Kirac, F., Kara, Y. E., & Akarun, L. (2011) · 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) · doi:10.1109/iccvw.2011.6130391 · Cited by 190
Cited by
A Low Power, Fully Event-Based Gesture Recognition System
Amir, A., Taba, B., Berg, D., Melano, T., McKinstry, J., Di Nolfo, C., Nayak, T., Andreopoulos, A., Garreau, G., Mendoza, M., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.781 · Cited by 1,017
Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State
Mandal, B., Li, L., Wang, G. S., & Lin, J. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2582900 · Cited by 328
Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition With Multimodal Training
Abavisani, M., Joze, H. R. V., & Patel, V. M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00126 · Cited by 190
IMU Sensor-Based Hand Gesture Recognition for Human-Machine Interfaces
Kim, M., Cho, J., Lee, S., & Jung, Y. (2019) · Sensors · doi:10.3390/s19183827 · Cited by 169
Vision-Based Driver Assistance Systems: Survey, Taxonomy and Advances
Horgan, J., Hughes, C., McDonald, J., & Yogamani, S. (2015) · 2015 IEEE 18th International Conference on Intelligent Transportation Systems · doi:10.1109/itsc.2015.329 · Cited by 72
Metadata
- Authors
- Ohn-Bar, E., & Trivedi, M. M.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2014-12
- Volume
- 15
- Issue
- 6
- Pages
- 2368-2377
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W1993666393
- Cited by
- 455
- References
- 5
- FWCI
- 54.69
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ohn-Bar, E., & Trivedi, M. M. (2014). Hand Gesture Recognition in Real Time for Automotive Interfaces: A Multimodal Vision-Based Approach and Evaluations. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2014.2337331
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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.
