Detection Of Moving Objects In Complex Scenes Based On Multiple Features

In order to enhance the integrity and accuracy of moving object detection in complex scenes, a multi features-based moving object detection method is proposed. The color feature is modeled by using the proposed adaptive Gaussian mixture model (GMM) algorithm. A kind of hysteresis multi -thresholds modeling method is used to model the scene background by adopting the color and improved local binary pattern (LEP) texture feature simultaneously. A neighborhood compensation strategy is adopted to combine the object regions obtained by the two-features extraction. The improved Kirsch edge detection method combined with the Canny thoughts is adopted in the edge extraction which eliminates the mistakenly detected ghost pixels and improves the edges of foreground objects. The experimental results show that the proposed method is superior to the traditional algorithms in the detection integrity and accuracy, and the real-time performance is also better.