School of Physical Education, Xinyang Normal University, Xinyang 464000, China.
School of Physical Education, Central China Normal University, Wuhan 430079, China.
Comput Intell Neurosci. 2022 Feb 15;2022:6986831. doi: 10.1155/2022/6986831. eCollection 2022.
Aiming at the detection of athletes in sports videos, an automatic detection method based on AMNN is proposed. The background image from the image sequence is obtained, the moving area is extracted, and the color information of pixels to extract the green stadium from the background image is used. In order to improve the accuracy of athletes' detection, the texture similarity measurement method is used to eliminate the shadow in the movement area, the morphological method is used to eliminate the cracks in the area, and the noise outside the stadium is removed according to the stadium information. Combined with the images of nonathletes, a training set is constructed to train the NN classifier. For the input image frames, image pyramids of different scales are constructed by subsampling and the positions of several candidate athletes are detected by NN. The center of gravity of candidate athletes is calculated, a representative candidate athlete is obtained, and then, the final athlete position through a local search process is determined. Experiments show that the system can accurately detect the motion shape of moving targets, can process images in real time, and has good real-time performance.
针对体育视频中的运动员检测问题，提出了一种基于 AMNN 的自动检测方法。从图像序列中获取背景图像，提取运动区域，利用像素的颜色信息从背景图像中提取绿色体育场。为了提高运动员检测的准确性，使用纹理相似性测量方法消除运动区域中的阴影，使用形态学方法消除区域中的裂缝，并根据体育场信息去除体育场外的噪声。结合非运动员的图像，构建一个训练集，用于训练 NN 分类器。对于输入的图像帧，通过子采样构建不同比例的图像金字塔，并通过 NN 检测几个候选运动员的位置。计算候选运动员的重心，得到一个有代表性的候选运动员，然后通过局部搜索过程确定最终的运动员位置。实验表明，该系统能够准确地检测运动目标的运动形状，能够实时处理图像，具有良好的实时性能。
