Application of Computer Image Recognition Technology in Campus Monitoring
- DOI
- 10.2991/ncce-18.2018.104How to use a DOI?
- Keywords
- Image Recognition; Intelligentization; Video Surveillance
- Abstract
Based on the application of computer image recognition technology in university campus monitoring, to automatically realize the intelligent management of university by image recognition technology, this scheme could be used in the practical application of campus situation monitoring, face recognition, campus anomaly monitoring, fire prevention and theft, etc. In the process of intelligent automatic monitoring, we could make use of sound, light, electricity, mobile communication and so on for early warning; it could also analyses the behavior characteristics of university personnel, and automatically form monitoring reports and reports. Finally, the Java language was used to implement the algorithm, and the video and audio stream scheme FFmpeg was used to intercept the monitored video. The image processing was mainly called open source cross platform computer vision library, Opens, to decompose and recognize the face in the image. The simulation experiment results showed that the image recognition algorithm based on image fingerprint had short operation time and high efficiency. This method can effectively monitor and monitor the abnormal changes of campus, face recognition, statistical time and the number of colleges and universities, and can monitor and judge the accidents such as fire and theft.
- Copyright
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Tiejun Feng AU - Yuyou He PY - 2018/05 DA - 2018/05 TI - Application of Computer Image Recognition Technology in Campus Monitoring BT - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018) PB - Atlantis Press SP - 643 EP - 647 SN - 1951-6851 UR - https://doi.org/10.2991/ncce-18.2018.104 DO - 10.2991/ncce-18.2018.104 ID - Feng2018/05 ER -