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Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces
- Source :
- Chinese Journal of Electronics. 29:1153-1164
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- Hand gesture recognition on the depth videos is a promising approach for automotive interfaces because it is less sensitive to light variation and more accurate than other traditional methods. However, video gestures recognition is still a challenging task since lots of interferences are induced by the uncorrelated gesture factors. Considering that if the displays are more relevant, the results will more accurate, so ResNext, a kind of compact and efficient neural network, is firstly used as feature extractor, then an improved weighted frame unification method is adopted to obtain the key frame samples, finally the Discriminant correlation analysis (DCA) is employed to fuse features for static data and dynamic data after conducting Feature embedding branch (FEB) on static data. The public dataset named Depth based gesture recognition database (DGRD) is used in this paper, but the dataset is a little small and the class distribution is largely imbalance, and we find the performance of ResNext degrades badly in the condition of imbalance problem although it achieves excellent result at sufficient training data. In order to conquer the disadvantages of limited dataset, a special loss function scheme combining the softmax loss and dice loss is proposed. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed method is more practical for gesture recognition and may be widely adopted by automotive interfaces.
- Subjects :
- Artificial neural network
business.industry
Computer science
Applied Mathematics
Dynamic data
Frame (networking)
Feature extraction
020206 networking & telecommunications
Pattern recognition
02 engineering and technology
Gesture recognition
Softmax function
0202 electrical engineering, electronic engineering, information engineering
Feature (machine learning)
Key frame
020201 artificial intelligence & image processing
Artificial intelligence
Electrical and Electronic Engineering
business
Subjects
Details
- ISSN :
- 20755597 and 10224653
- Volume :
- 29
- Database :
- OpenAIRE
- Journal :
- Chinese Journal of Electronics
- Accession number :
- edsair.doi...........be72e41a586f2a2690d02cf385bdd44d