体育训练视频中的运动目标检测往往存在目标快速移动、目标遮挡和场景变化等问题,导致运动目标检测的难度增大.为了解决这些问题,提出了基于卡尔曼(Kalman)滤波算法的体育训练视频中运动目标检测的方法.为了提升体育训练视频图像的质量,首先对体育训练视频的图像进行一系列预处理(包括灰度变换、轮廓对比增强和噪声抑制等);然后借助混合高斯模型(Gaussian mixture module,GMM)有效提取体育训练视频的前景信息;为了精准捕捉体育训练视频中的运动目标,运用三帧差分法设定Kalman滤波器的初始状态,利用高效检测算法准确获取每一帧图像中运动目标的观测位置;随后将初始状态和观测位置的数据输入Kalman滤波器;最后在Kalman滤波器中,结合上一帧的预估值和当前帧的监测值,对体育训练视频中运动目标的当前状态进行精确估算和优化,并对下一帧的状态进行预测,从而实现了对运动目标的持续跟踪与精准预测.实验结果表明,与基于特征融合的全卷积孪生网络(siamese full convolution,Siamfc)目标追踪算法和基于连续自适应均值漂移(continuously adapting mean shift,Camshift)的均值漂移(mean shift,Meanshift)改进算法相比,采用基于Kalman滤波算法的运动目标检测方法对运动目标的重叠精度(overlap precision,OP)和中心位置误差(center location error,CLE)进行检测,不仅能够有效检测到体育训练视频中的运动目标,还可表现出较高的准确性和实时性.
Moving Object Detection in Sports Training Videos Based on Kalman Filtering Algorithm
The detection of moving targets in sports training videos often faces problems such as target rapid move-ment,target occlusion,and scene changes,which increase the difficulty of moving target detection.To address these issues,a method for detecting moving targets in sports training videos based on Kalman filtering algorithm was pro-posed.Inorder to improve the quality of sports training video images,a series of preprocessing steps(including grayscale transformation,contour contrast enhancement,and noise suppression)were first performed on the images of sports training videos;Then,the Gaussian mixture module(GMM)was used to effectively extract foreground in-formation from sports training videos;In order to accurately capture the moving targets in sports training videos,the three frame difference method was used to set the initial state of the Kalman filter,and an efficient detection algo-rithm was used to accurately obtain the observation position of the moving targets in each frame of the image;Then the initial state and observation position data into the Kalman filter was inputted;Finally,the Kalman filter combines the estimated value of the previous frame with the monitored value of the current frame to accurately estimate and op-timize the current state of the moving target in the sports training video,and predicted the state of the next frame,thus achieving continuous tracking and accurate prediction of the moving target.The experimental results indicated that compared with the siamese full convolution(SiamFC)object tracking algorithm based on feature fusion and the mean shift(Meanshift)improvement algorithm based on continuous adaptive mean shift(Camshift),the moving ob-ject detection method based on Kalman filter could effectively detect the overlap precision(OP)and center location er-ror(CLE)of moving objects in sports training videos,and also exhibit high accuracy and real-time performance.
Kalman filtering algorithmsports training videosmoving object detectionview preprocessingthree frame difference methodGaussian mixture module model
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