Adiguna, Richard and Soelistio, Yustinus Eko (2018) CNN Based Posture-Free Hand Detection. In: 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), 24-26 July 2018, Bali.
Full text not available from this repository.Abstract
Although many studies suggest high performance hand detection methods, those methods are likely to be overfitting. Fortunately, the Convolution Neural Network (CNN) based approach provides a better way that is less sensitive to translation and hand poses. However the CNN approach is complex and can increase computational time, which at the end reduce its effectiveness on a system where the speed is essential.In this study we propose a shallow CNN network which is fast, and insensitive to translation and hand poses. It is tested on two different domains of hand datasets, and performs in relatively comparable performance and faster than the other state-of-the-art hand CNN-based hand detection method. Our evaluation shows that the proposed shallow CNN network performs at 93.9% accuracy and reaches much faster speed than its competitors.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems |
| Divisions: | Faculty of Engineering & Informatics > Information System |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 14 Dec 2021 07:56 |
| Last Modified: | 14 Dec 2021 07:56 |
| URI: | https://kc.umn.ac.id/id/eprint/19642 |
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