复杂背景下基于汉明距离约束的Hash_LBP运动目标快速检测算法

Fast Hash_LBP Moving Target Detection Algorithm Based on Hamming Distance Constraint in Complex Background

自然环境复杂多变,存在复杂天气如雨雪雾,草木摇晃和水面波动等大量动态背景并且光线不断变化,因此对噪声以及背景的抑制一直是复杂场景中运动目标检测的首要难题。为了抑制动态背景、慢速目标被吸收以及图像编码噪声等问题,在保证实时性的基础上,提出了一种基于纹理特征的自适应阈值运动目标检测算法。所提算法将感知哈希算法与局部二值模式结合,提出了一种改进的Hash_LBP算法并使用汉明距离进行约束,得到输入图像的局部二值模式值进行频次统计后,完成背景建模和前景提取。实验结果表明,所提算法对于红外和可见光等多种复杂背景,能够有效地抑制噪声、光照变化和动态背景,快速准确提取前景目标。

Moving target detection algorithm is to detect the changing region in the input image sequence,so as to extract the target from the background.It is very important for subsequent target recognition and tracking.Due to the complex and changeable natural environment,there is a large number of dynamic backgrounds and changing lights in complex weather,such as rain,snow,fog,vegetation shaking and water surface fluctuation,which has always been the primary problem of moving target detection in complex scenes.In this paper,when the camera has a fixed angle of view,the background modeling algorithm is adopted.To suppress the problems of dynamic background,slow target absorption and image coding noise,based on ensuring real-time performance,the time-frequency domain and frequency-domain characteristics of the input image are analyzed.Because the dynamic background fluctuates in a certain gray range.Through the texture features processed by LBP,the influence of illumination change can be suppressed.An adaptive threshold moving target detection algorithm based on texture features is proposed.First of all,the algorithm converts the input image sequence into a grayscale image,and uses the perceptual hashing algorithm to calculate the average pixel value in the window of 3×3 to remove the highfrequency details in the image part and improve the calculation speed.Then,the dynamic background and noise are processed preliminarily.The frequency domain of the input image is analyzed,and the maximum frequency of the gray frequency distribution histogram of each pixel is counted as the reference background and compared with each frame to obtain the difference matrix.The standard deviation of all values greater than 10 and less than 100 in the difference matrix is calculated as the adaptive threshold,and the gray value of each frame is corrected.Then the dynamic background and noise are processed a second time.Firstly,the Local Binary Pattern(LBP)is used to process the reference background and each frame of the image after preliminary processing to obtain the LBP value.Then,the hamming distance 3 was selected as the threshold to correct the LBP value of each frame.Finally,the LBP texture feature map of each frame is analyzed from the frequency domain,and the background modeling and foreground extraction are carried out according to the polymorphic frequency distribution.To suppress the influence of illumination change,dynamic background and noise on foreground extraction,this paper proposes an improved Hash_LBP algorithm combined with a perceptual hash algorithm and uses Hamming distance to constrain it.Experiments show that the proposed algorithm can effectively suppress noise,illumination change and dynamic background in a variety of complex scenes such as infrared and visible light,quickly and accurately extract foreground targets,and the algorithm is also effective in dynamic background suppression for ViBe and GMM algorithms.