Abstract
There are many drawbacks such as clustering, background updating, inaccurate testing results, and low anti-interference performance in traditional moving target detection theory. In our study, a background subtraction method to automatically capture the basketball shooting trajectory was used to eliminate the drawbacks of the fixed-point shooting system such as cumbersome installation and time and manpower consumption. It also can improve the accuracy and efficiency of moving target detection. We also synthetically compared to common methods including the optical flow method and interframe difference method. Results showed that the background subtraction method has better accuracy with an accuracy rate over about 90% than the interframe subtraction method (88%) and the optimal flow method (85%) and presents excellent robustness with considering variable speed and nonrigid objects. Meanwhile, the automatic detection system for basketball shooting based on background subtraction is built by coupling background subtraction with detection characteristics. The system detection speed built is further accelerated, and the image denoising is improved. The trajectory error rate is about 0.3, 0.4, and 0.5 for the background subtraction method, interframe subtraction method, and optimal flow method, respectively.
Highlights
In recent years, with the development of computer vision technology and video surveillance, the moving target detection technology (MTT), a significant part of them, has gradually attracted researchers’ attention and has been widely applied in different domains such as defense and security monitoring [1,2,3]
Subtraction Method. e core theory of background subtraction is matching the current frame with the reference image from the background model and calculating the similarity value between the image point and that in the background model. e mathematical expression is as follows:
After constructing the background model and subtracting each pixel in the video image sequence from the background model built, if the pixel value exists in the same location between the image sequence and background model, the pixel point is regarded as a background point, and as a moving target otherwise
Summary
With the development of computer vision technology and video surveillance, the moving target detection technology (MTT), a significant part of them, has gradually attracted researchers’ attention and has been widely applied in different domains such as defense and security monitoring [1,2,3]. In order to extract quickly and accurately the position and outlook feature of the basketball from the video/image sequence, a binarization procedure is used for the grayscale image. In this procedure, the selection of segmentation threshold is first in the process of binarization. E interframe subtraction method subtracts pixels between continuous 2 or 3 frames from the video image sequence and compares those with the threshold preset to extract moving regions from image information. In order to detect the basketball shooting trajectory accurately, we first need to know about the feature of basketball shooting; after identifying the characteristics of shooting, the position of the capture device can be further set. MEMS sensors have the advantages of wireless transmission, low cost, superior trajectory capture effect, and convenient operation, which are widely used
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