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Conference Paper
Soccer Object Motion Recognition based on 3D Convolutional Neural Networks
- Authors
- Jiwon Lee, Do-Won Nam, Wonyoung Yoo, Yoonhyung Kim, Minki Jeong, Changick Kim
- Issue Date
- 2018-09
- Citation
- Federated Conference on Computer Science and Information Systems (FedCSIS) 2018, pp.129-134
- Language
- English
- Type
- Conference Paper
- Abstract
- Due to the development of video understanding and big data analysis research field using deep learning technique, intelligent machines have replaced the tasks that people performed in the past in various fields such as traffic, surveillance, and security area. In the sports field, especially in soccer games, it is also attempting quantitative analysis of players and games through deep learning or big data analysis technique. However, because of the nature of soccer analysis, it is still difficult to make sophisticated automatic analysis due to technical limitations. In this paper, we propose a deep learning based motion recognition technique which is the basis of high level automatic soccer analysis. For sophisticated motion recognition, we maximize recognition accuracy by sequentially processing the data in three steps: data acquisition, data augmentation, and 3D CNN based motion classifier learning. As can be seen from the experimental results, the proposed method guarantees real-time speed performance and satisfactory accuracy performance.
- KSP Keywords
- 3D CNN, 3D convolutional neural network, Accuracy performance, Automatic analysis, Big Data analysis, Convolution neural network(CNN), Data Acquisition(DAQ), Data Analysis technique, Data Augmentation, Intelligent machines, Motion Recognition
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
