Family Anomalies Intelligent Monitoring System Design And Realization

At present, the demand for the function of monitoring system has been developing toward diversity and systematism. Users hope that their remote access won't be subject to geographical constraints in the future, so that they can monitor whatever they want anytime and anywhere. They also hope that a monitor system possesses a Embedded System in miniature. What's more, the future video monitor system has to be more intelligent, that is, it can detect all abnormity and then sends a message of alarm and provides useful information which will help deal with the crisis more effectively.In order to satisfy the demands above, this thesis designed an embedded intelligent network monitoring system which was composed of three parts, that was, server-side video monitor system, video monitor system network client, and fire intelligent detection algorithm. Video monitor system server-side and client-side were directly connected to the Internet through TCP/IP protocol to transfer data. The following parts would introduce the three parts in detail.First, Video monitor system server side was the terminal of entire monitoring system, and it was designed to collect real-time video data and start the intelligent monitor and network functions. It would call the fire intelligent detection algorithm when implementing, and then find out abnormity by judging the video data it collected. Once fire was detected, it would alarm and kinescope what happened automatically. Second, Video system network client was designed based on windows system, and it would transfer command character through TCP socket and real-time video image data with server-side. If the video surveillance network client sent connection requests, video surveillance server-side would sent back encoded video data through the socket to the remote client player. Third, the fire intelligent detection algorithm was used to detect whether there was fire in room, making full use of the image color information, and extract suspected fire area, which made the demarcation of identified area and regional background available to the color image segmentation. After that, threshold segmentation method, specifically, stable Otus Law, was used for multicolor image segmentation, and dynamic characteristics of the fire area was extracted for fire judgments. Invasion and the loss of object detection algorithm is mainly used for indoor testing was broken into, and monitor whether there are objects missing region. It is based on the color image histogram HS, calculated through the clustering of its color clusters, and then the color image according to the similarity of cluster computing, and finally to determine whether there are abnormal.