Multiple Moving Targets Detection Technology

Moving Object detection is a crucial researching issue in computer vision field, which has widely applicative prospects in video surveillance, transportation, medical research and the military field etc. With in-depth study about this theory conducted by the researchers both domestic and overseas, moving object detection technology has been rapidly developed.The paper puts the intelligent surveillance video analysis techniques as research background, aiming the true complexity of the environment, discusses the interfering factors such as the illumination changes, shadows, scene itself changes and moving background. In response to these problems, we study some commonly used moving object detection algorithms and give the comparative analysis of these theoretical principles and results. Subsequently, this paper sums up the characteristics of their respective. Particularly, we conduct a research into the background modeling algorithm in moving object detection, discuss background subtraction and frame difference method respectively. Besides that, we analyze the theoretical results and then draw the conclusion that the algorithms have pertinence to the scenes. On the foundation of this, in this paper, we propose a region of motion and change detection algorithm of moving object algorithm. The algorithm sets up two background subtractions and frame difference methods. At the region changing level, it integrates background subtraction into implement foreground detection. In order to make sure that the background can inhibit the effect of shadows. Photometric invariant color in RGB color space is used in color model matching for making background robust to illumination changes. In the process of removing the shadow of this paper, based on the HSV space and the RGB space of the method of combination, we remove the shadow for being a real prospect of goals effectively.In this paper, we implement the proposed method and evaluate the performance of our method on real scenes. The experimental results prove that our method can apply to the complex scene under illumination conditions, at the same time, it can reduce external interference, such as the light changes, moving background (branches swaying) and shadow impact. Finally, this paper compares the proposed methods with Gaussian mixture modeling, by analyzing the contrast results. Additionally, the compares obtain the proposed methods having strong accuracy and robustness under complex environment containing shadows and moving background.