Falling-Action Analysis Algorithm Based on Convolutional Neural Network
Wei Liu, Jie Guo, Zheng Huang, Weidong Qiu
Available Online October 2016.
- https://doi.org/10.2991/ceie-16.2017.5How to use a DOI?
- Falling-Action Analysis; Deep Learning; Convolutional Neural Network
- This paper proposes a deep learning method - convolutional neural network to analyze human falling-action in video surveillance, so that we can recognize the falling-action of human body accurately in the shortest time. Firstly, vibe algorithm is used to extract the foreground and some methods of image preprocessing are employed to optimize the moving target. Then the moving target is fed into the convolutional neural network which extracts the features of various actions (including sitting, crouching, bending, falling) and classifies these actions. It is proved by experiments that our method is accurate and competitive compared with the current method to falling-action recognition.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Wei Liu AU - Jie Guo AU - Zheng Huang AU - Weidong Qiu PY - 2016/10 DA - 2016/10 TI - Falling-Action Analysis Algorithm Based on Convolutional Neural Network BT - Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016) PB - Atlantis Press SP - 37 EP - 42 SN - 2352-5401 UR - https://doi.org/10.2991/ceie-16.2017.5 DO - https://doi.org/10.2991/ceie-16.2017.5 ID - Liu2016/10 ER -