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A Hybrid Approach to Hand Detection and Type Classification in Upper-Body Videos
dc.creator | Papadimitriou K., Potamianos G. | en |
dc.date.accessioned | 2023-01-31T09:42:22Z | |
dc.date.available | 2023-01-31T09:42:22Z | |
dc.date.issued | 2019 | |
dc.identifier | 10.1109/EUVIP.2018.8611755 | |
dc.identifier.isbn | 9781538668979 | |
dc.identifier.issn | 24718963 | |
dc.identifier.uri | http://hdl.handle.net/11615/77588 | |
dc.description.abstract | Detection of hands in videos and their classification into left and right types are crucial in various human-computer interaction and data mining systems. A variety of effective deep learning methods have been proposed for this task, such as region-based convolutional neural networks (R-CNNs), however the large number of their proposal windows per frame deem them computationally intensive. For this purpose we propose a hybrid approach that is based on substituting the 'selective search' R-CNN module by an image processing pipeline assuming visibility of the facial region, as for example in signing and cued speech videos. Our system comprises two main phases: preprocessing and classification. In the preprocessing stage we incorporate facial information, obtained by an AdaBoost face detector, into a skin-tone based segmentation scheme that drives Kalman filtering based hand tracking, generating very few candidate windows. During classification, the extracted proposal regions are fed to a CNN for hand detection and type classification. Evaluation of the proposed hybrid approach on four well-known datasets of gestures and signing demonstrates its superior accuracy and computational efficiency over the R-CNN and its variants. © 2018 IEEE. | en |
dc.language.iso | en | en |
dc.source | Proceedings - European Workshop on Visual Information Processing, EUVIP | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062704702&doi=10.1109%2fEUVIP.2018.8611755&partnerID=40&md5=04ec8ffe89413905cfa1044792e51068 | |
dc.subject | Adaptive boosting | en |
dc.subject | Computational efficiency | en |
dc.subject | Convolution | en |
dc.subject | Data mining | en |
dc.subject | Deep learning | en |
dc.subject | Digital storage | en |
dc.subject | Face recognition | en |
dc.subject | Human computer interaction | en |
dc.subject | Information filtering | en |
dc.subject | Kalman filters | en |
dc.subject | Neural networks | en |
dc.subject | Petroleum reservoir evaluation | en |
dc.subject | Pipeline processing systems | en |
dc.subject | Convolutional neural network | en |
dc.subject | Data mining system | en |
dc.subject | Hand detection | en |
dc.subject | Image processing pipeline | en |
dc.subject | Kalman-filtering | en |
dc.subject | Learning methods | en |
dc.subject | Segmentation scheme | en |
dc.subject | Type classifications | en |
dc.subject | Palmprint recognition | en |
dc.subject | Institute of Electrical and Electronics Engineers Inc. | en |
dc.title | A Hybrid Approach to Hand Detection and Type Classification in Upper-Body Videos | en |
dc.type | conferenceItem | en |
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