Optimization of Surveillance Image Recognition of Civil Aviation Airport

Background: In civil aviation information monitoring system, optimization of image recognition is applied to promote monitoring of and control over passenger mobility. The traditional image recognition by video surveillance cannot effectively detect abnormal behaviors or explosives, as described in various patents. Method: In this paper, the author proposes a method for the optimization of surveillance image recognition in civil aviation airport based on contourlet domain edge detection. Firstly, an overall model of surveillance image recognition is established and statistically significant probability analysis and other data integration methods are employed to realize comprehensive treatment of visual images. In order to enhance the light-and-shade contrast of moving regions in the images and make images smoother, we must evaluate edge position information of surveillance images, extract the lowfrequency parts and signals to enhance contrast and promote image recognition capability. Results: Simulation experiment proved that this method produced better image recognition results and could effectively detect abnormal behaviors and violent terrorists. Conclusion: It is a superior algorithm, which is of great importance to ensure the safety of airports.