为提高复杂场景下运动目标检测的完整性和准确性,提出了一种多特征结合的运动目标检测方法。提出了一种自适应的高斯混合建模算法对颜色特征进行建模;通过滞后多阈值建模的方法,同时利用颜色和改进的局域二值模式纹理特征对环境背景进行了建模,并采用邻域补偿策略将基于两种特征提取得到的目标区域进行了结合;采用结合Canny思想改进的Kirsch方法进行了边缘提取,消除了鬼影误识别像素,改善了前景目标边缘。实验结果表明,所提方法在运动目标检测的完整性、准确性等指标上优于传统算法的,实时性也较好。

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 (LBP) 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.