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Abstract
The paper is dedicated to proposing and evaluating a number of convolutional neural network architectures for calculating a multiple regression on 3D coordinates of human body joints tracked in a single low resolution depth image. The main challenge was to obtain a high precision in case of a noisy and coarse scan of the body, as observed by a depth sensor from a large distance. The regression network was expected to reason about relations of body parts based on depth image, and to extract locations of joints. The method involved creation of a dataset with 200,000 realistic depth images of a 3D body model, then training and testing numerous CNN architectures. The results are included and discussed. The achieved accuracy was similar to a reference Kinect algorithm results, with a great advantage of fast processing speed and significantly lower requirements on sensor resolution, as it used 100 times less pixels than Kinect depth sensor.
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Keywords
Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- 2018 11th International Conference on Human System Interaction (HSI) strony 118 - 127
- Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Szczuko P.: CNN Architectures for Human Pose Estimation from a Very Low Resolution Depth Image// 2018 11th International Conference on Human System Interaction (HSI)/ : , 2018, s.118-127
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/hsi.2018.8431338
- Verified by:
- Gdańsk University of Technology
Referenced datasets
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