| As one of the core researches in the image processing and computer vision technology researches, moving target detection and tracking has been widely applied in many fields, such as video surveillance, navigation guidance, medical diagnostics, and so on.The target detection algorithm based on gray value can be designed easily and used widely, but the color components of the image are covered under the gray feature. Therefore, the target detection under the color space is becoming a research hot spot.This thesis presents an adaptive target detection method that is based on the RGB component. This method uses the RGB pixel value as the third feature information group, and it adjusts the proportion of the three components adaptively according to the background color of the image composition. When the backgrounds are different, this method calculates the difference between the three groups, and regards the sum of the absolute difference value of the three groups as the difference between pixels.This method also uses the adaptive threshold for difference image to inspect in order to achieve the target binary separation. To enhance the detection effect under the complex background, this method improves the mean background method, and it also builds the background model by the use of average of the backboard removal method of the target area.Mean Shift tracking algorithm is simple in principle and real-time, but when the target is blocked, it is easily to lose the track of the target. Therefore, the state variable is updated and modified and the estimated value of the current time to the next moment is calculated by introducing the Kalman filter to examine the estimated value of the previous time to the current time and the observed value of the current time; then the adoption of Mean Shift to carry on iterative calculation towards the predicted area can track the target under complete block continuously and stably, which promotes significantly the tracking effects. |
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