Spatio-temporal feature points detection and extraction based on convolutional neural network
Chaoyu Yang, Qian Liu, Yincheng Liang
Available Online June 2015.
- https://doi.org/10.2991/icecee-15.2015.83How to use a DOI?
- Spatio-temporal feature points detection and extraction based on convolutional neural network
- Convolutional neural network is a kind of deep learning model, but it only act on a single image generally. This paper expands the convolutional neural network, studies common spatio-temporal features detection and extraction algorithm, proposes a model for detecting and extracting spatio-temporal features based on convolutional neural network, applicates convolutional neural network in action recognition. This model use a plurality of consecutive video frames as input, extract image feature and time dimension information from sequent video frames, convolute and sub sampling alternately, extract a variety of advanced complex abstract features gradually. Experiments show that spatio-temporal convolution neural network has improved ability to classifing and learning.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Chaoyu Yang AU - Qian Liu AU - Yincheng Liang PY - 2015/06 DA - 2015/06 TI - Spatio-temporal feature points detection and extraction based on convolutional neural network BT - Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics PB - Atlantis Press SP - 400 EP - 403 SN - 2352-538X UR - https://doi.org/10.2991/icecee-15.2015.83 DO - https://doi.org/10.2991/icecee-15.2015.83 ID - Yang2015/06 ER -