Research On Automatic Monitoring Technology Of Grain Conveyor Based On Video Image

Our country is a considerable nation of grain production as well as grain consumption. Every procedure from production to processing then to transportation of grain is closely related to people’s life. In the process of grain transportation, the belt of delivery device will probably happens to slip, slant to one side, rupture or occur other breakdowns. All these malfunctions will lead to evident grain leakage in grain delivery site. If effective solution won’t be executed promptly, it will without doubt result in grain accumulate that effect the efficiency of grain transportation heavily. Therefore, it is necessary to detect the state of grain delivery site so that the staff will be able to take measures in case of any breakdown in time.With the rapid development of video technology and electronic information technology, video surveillance system has been widely used in every fields. The use of video technology for automatic monitoring of the actual situation has gradually become a mainstream trend in the field of video monitoring.According to the actual situation in the field of grain delivery combined with the development of video monitoring technology, this paper study an automatic monitoring system for grain delivery device based on video and image technology.Firstly, the paper introduces the related video and image processing technology, lays the theoretical foundation for the realization of the subsequent algorithms. Secondly, the paper studies three currently-used motion detection algorithms:optical flow, frame difference method, background subtraction method, comparing the advantages and disadvantages of the three algorithms through the simulation results. Combined with the advantages and disadvantages of three motion detection algorithms and the basic requirements of grain delivery device fault monitoring during operation, the paper proposes a motion detection algorithm based on three-frame difference and background subtraction combination, the algorithm is robust to the changes in illumination. The background subtraction method using a modified gaussian mixture model to model and update the background, compared to the traditional method of gaussian mixture model, the modified gaussian mixture model can improve the algorithm in real time. Then, completing designing the main monitoring function modules of the monitoring system by using Visual C ++ 6.0 software development platform combines the auxiliary library OpenCV. Finally, set up the experimental platform to verify the method for achieving grain delivery device automatic monitoring which the paper proposes. Experimental results show that the motion detection algorithm proposed by the paper has certain validity for the monitoring to grain leakage caused by the belt of delivery device happens to slip, slant to one side and rupture during operation.