Using Camshift and Kalman Algorithm to Trajectory Characteristic Matching of Basketball Players

Shuang Liang,Yang Li

doi:10.1155/2021/4728814

Abstract

Because of its unique charm, sports video is widely welcomed by the public in today’s society. Therefore, the analysis and research of sports game video data have high practical significance and commercial value. Taking a basketball game video as an example, this paper studies the tracking feature matching of basketball players’ detection, recognition, and prediction in the game video. This paper is divided into four parts to improve the application of the interactive multimodel algorithm to track characteristic matching: moving object detection, recognition, basketball track characteristic matching, and player track characteristic matching. The main work and research results of each part are as follows: firstly, the improved K-means clustering algorithm is used to segment the golf field area; then, HSV is combined with the RGB Fujian value method to eliminate the field area; at last, straight field lines were extracted by Hough transform, and elliptical field lines were extracted by curve fitting, and the field lines were eliminated to realize the detection of moving objects. Seven normalized Hu invariant moments are used as the target features to realize the recognition of moving targets. By obtaining the feature distance between the sample and the template, the category of the sample is judged, which has a good robustness. The Kalman filter is used to match the characteristics of the basketball trajectory. Aiming at the occlusion of basketball, the least square method was used to fit the basketball trajectory, and the basketball position was predicted at the occlusion moment, which realized the occlusion trajectory matching. The matching of players’ track characteristics is realized by the CamShift algorithm based on the color model, which makes full use of players’ color information and realizes real-time performance. In order to solve the problem of occlusion between players in the track feature matching, CamShift and Kalman algorithms were used to determine the occlusion factor through the search window and then weighted Kalman and CamShift according to the occlusion degree to get the track feature matching result. The experimental results show that the detection time is greatly shortened, the memory space occupied is small, and the effect is very ideal.

Highlights

Sports video is a kind of important media data, which has a large audience and a huge application prospect and is widely concerned by the academic and industrial circles
For the K-means clustering algorithm can automatically select the number of clustering and clustering results depending on the initial clustering points’ shortcomings, this paper proposes a K-means clustering algorithm based on fuzzy set theory, according to the original image histogram to determine the clustering number and initial clustering points, realizes the clustering number and the automatic selection of the initial clustering points, and has obtained the good segmentation effect
The segmentation and track characteristic matching of the interested object-player in the basketball game, which is the most widely watched sports video, are studied, which lays a foundation for the further analysis of the sports video

Summary

Introduction

Sports video is a kind of important media data, which has a large audience and a huge application prospect and is widely concerned by the academic and industrial circles. In the training video of athletes, coaches can obtain human motion parameters by adopting relevant processing methods such as segmentation and track characteristic matching. Erefore, it is of great practical value and significance to detect, extract, locate, and match the trajectory characteristics of moving objects in a basketball game video. (3) In the aspect of target recognition, 7 Hu invariant moments were used to identify players and balls, which improved the shortcomings of the original identification method that could not correctly identify objects with rotation and size changes. E Kalman filter is an algorithm for linear minimum variance error estimation of the state sequence of the dynamic system, which is calculated by the recursive filtering method It has the characteristics of small computation amount and realtime computation and has quite good track characteristic matching effect for objects with approximately uniform speed motion.

Trajectory Matching of Basketball Players

Result

Conclusion