基于改进的Gabor滤波和区域生长的目标检测

Target detection based on improved gabor filter and region growing

针对目标检测中背景的相对运动、检测物体相似性、外界光线变化等因素的影响,提出了一种基于改进的Gabor滤波和区域生长的目标检测方法。首先将输入的视频图像的相邻两帧做差,然后将得到差值图像中的Lab模型L分量进行Gabor滤波处理,根据不同的场景选择合适的参数,以提取目标的显著区域。为了兼顾运行速度和目标的显著特性,所以选取图像以某一灰度值的像素点为种子点,将灰度值差值在某一范围内的点进行区域生长操作,得到更为准确的目标。接着,为了去除干扰,进行腐蚀膨胀等一系列操作。最后将8个方向的滤波合并在一起,用外接矩形将符合要求的目标区域标出,达到目标检测的目的。通过参数调整和实验验证,相比传统检测算法的低准确率,此方法的目标检测率提高至90.89%,并且在光照、背景等外界因素干扰的情况下能具有良好的鲁棒性。

A target detection method based on improved Gabor filtering and regional growth is proposed for the influence of the relative motion of the background,the similarity of the object and the change of the external light.First it will do subtraction between two adjacent frames of the input video image,and then the L component of the Lab model in the difference image is subjected to Gabor filter processing,then we need to choose the right parameters according to different scenarios to extract the salient region of the target.In order to consider the operating speed and the significant features of the target,we need to select a pixel with a gray value as the seed point,and for the purpose of obtaining a more accurate target,we need to perform a region growing operation at a point within a certain range of gray value difference.Next,we need use corrosion and swelling to remove interference.Finally,to achieve the purpose of testing,the eight directions of filtering should be merged together,and the circumscribed rectangle is used to mark the target area that meets the requirements.Through parameter adjustment and experimental verification,the target detection rate of this method is improved to 90.89%compared with the low accuracy of the traditional detection algorithm,and it can have good robustness under the interference of external factors such as illumination and background.