结合HED网络和双阈值分割的GMM目标检测算法

GMM在目标检测过程中容易受到灯光、目标颜色与背景颜色相似、目标阴影和拍摄高度等因素的干扰。针对以上问题，本文提出一种结合改进HED网络和OTSU双阈值分割的GMM算法。首先，改进模型针对视频帧的背景、噪声、前景目标进行双阈值分割，合理选取高斯模型个数。其次，利用HED网络对输入图片进行边缘检测，将HED网络检测的边缘结果和双阈值分割的GMM检测结果进行“与”运算，得到最终目标检测结果。通过实验验证，改进算法的检测率更高，目标较小时检测轮廓更加完整，检测效果更好。

Target Detection Algorithm of Gaussian Mixture Model Combined with HED Network and Double Threshold Segmentation

In the process of target detection, the GMM is easily interfered by lighting, the similarity of target color and background color, target shadow and shooting height. Aiming at the above problems, a GMM algorithm is combined with improved HED network and OTSU double threshold segmentation is proposed. First, the improved model divides the background, noise and foreground targets of video frame by double threshold, and reasonably selects the number of GMM. Secondly, HED network is used for edge detection of the input images.The “and” operation of edge result detected by the HED network and the GMM detection result of the double threshold segmentation is completed to obtain the final target detection result. Experimental results show that the improved algorithm has a higher detection rate, a more complete detection profile “and” a better detection effect.