Design And Implementation Of Intelligent Monitoring System Based On 3G Network

The popularity and application of 3G network is accelerating another process of Internet Age, which has opened up a new era of network.Currently.The video surveillance based on 3G technology has become main stream in the field of video surveillance. Traditional video surveillance system is limited to a fixed monitoring,few network monitoring and mobile surveillance functions are achieved.There are some limitations in its Applications.3G network based on remote monitoring can realize real-time, mobile, interactive remote monitoring management andwell make up for the deficiencies of the traditional monitoring.Thereby, improving the quality of monitoring and management,better services for the monitoring and management work is necessary.According to the development status and trends of the current video surveillance, an embedded video monitoring system based on 3G networks has been proposed.The system can realize three video surveillance modes,such as the phone monitoring,the internet monitoring and the local computer multi-window monitoring.The key technology is described in detail in this thesis, including 3G network transmission,video processing,embedded and streaming media technology.This thesis introduces the main functions of Hi3510 module and EM770 W 3G wireless network module, which has built a RTP/RTCP network protocol, and the 3G network communication is developed based on Socket programming, the video compression coding technique is based on H.264, the driver software is developed based on Linux system platform.Finally,the functions including the video input, compression encoding, transmission and background monitoring display have been achieved.System testing shows that the 3G network embedded video monitoring system is able to achieve video multi-screen monitor,the image set,video recording,video playback,unusual circumstance can be as a reminder function on mobile,local computer or other online clients.In addition, the system has good scalability in order to facilitate the further function optimization.