Real-time Scene Understanding System

Scenery understanding system, which this paper researched as a kind of intelligent recognition system, may respond to the change of the surrounding scenery by real time. Our task is researching the function of scenery understanding in detail, and being able to process, analyze, judge the scenery's images which are obtained by video camera, so the scenery understanding system can recognize the important information, for example, human, non human, smoke, fire, etc, and raise the alarm.First, we present a real-time human shape recognition method, which can monitor the object moving in the shooting scope of the video camera, and recognize the object being human or not. Following are the approaches used in this paper. First, we pretreat the image which include the other object besides background, that is ,getting rid of background image and obtain the shape image of the object. Second, we calculate the seven moment invariants of that image. Finally, we use Beyas assorting approach as main method assort the moment invariants and use other assistant methods to correct the result in order to recognize the human shape and judge whether human has passed or not.In this paper, we also researched the recognition of fire and smoke. The traditional fire alarming system is usually based on infrared sensor and smog sensor, but if it is in big space, the lost rate is high. This paper use effective character of the fire, for example, color information, instability and comparability of the edge of image sequence to realize the automatic alarming for fire.For the recognition of smog, the system uses the technique of integrating the edge abstracting with count histogram. First, abstract the edge of background image, and count the percentage of edge pixels in it. Second, abstract the edge of current frame image of sequence, and count the percentage of edge pixels. Finally, compare these two percentages to judge whether there is much smog or not.Many experiments proved that this scenery comprehending system is able to work well with a low lost rate. At the same time, the system is developable, that is,the system can be added or improved all sorts of scenery understanding functions based on original functions, so it can be applied in many fields.