Research on sports video detection technology motion 3D reconstruction based on hidden Markov model

Lu, Yuanxu; An, Shuyang

doi:10.1007/s10586-020-03097-z

Cited by 21 publications

(10 citation statements)

References 22 publications

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“…At present, many national departments and scholars have done a lot of research on motion target analysis and its technology. In 1997, the Defense Advanced Research Projects Agency of the United States established the Visual Surveillance and Monitoring (VSAM) system, which was led by Carnegie Mellon University Robot and participated by Sarnoff Company and so on [1]. e system can be used for battlefield situation analysis, safety monitoring in important places, and monitoring of refugee flow.…”

Section: Introductionmentioning

confidence: 99%

Sports Video Augmented Reality Real-Time Image Analysis of Mobile Devices

Wang

Zhao

2021

Mathematical Problems in Engineering

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Sports video is loved by the audience because of its unique charm, so it has high research value and application value to analyze and study the video data of competition. Based on the background of football match, this paper studies the football detection and tracking algorithm in football game video and analyzes the real-time image of real-time mobile devices in sports video augmented reality. Firstly, the image is preprocessed by image graying, image denoising, image binarization, and so on. Secondly, Hough transform is used to locate and detect football, and according to the characteristics of football, Hough transform is improved. Based on the good performance of SIFT algorithm in feature matching, a football tracking algorithm based on SIFT feature matching is proposed, which matches the detected football with the sample football. The simulation results show that the improved Hough transform can effectively detect football and has good antijamming performance. And the designed football tracking algorithm based on SIFT feature matching can accurately track the football trajectory; therefore, the football detection and tracking algorithm designed in this paper is suitable for real-time football monitoring and tracking.

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Section: Introductionmentioning

confidence: 99%

Sports Video Augmented Reality Real-Time Image Analysis of Mobile Devices

Wang

Zhao

2021

Mathematical Problems in Engineering

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“…Assuming that the Lambert model can be used in the imaging process of the camera photoreceptor, an integral formula can be used to describe the response of each color channel of the photoreceptor [17,18].…”

Section: Eigen Imagementioning

confidence: 99%

[Retracted] Detection and Adaptive Video Processing of Hyperopia Scene in Sports Video

Qing-jie

Dong

2021

Complexity

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In the research of motion video, the existing target detection methods are susceptible to changes in the motion video scene and cannot accurately detect the motion state of the target. Moving target detection technology is an important branch of computer vision technology. Its function is to implement real-time monitoring, real-time video capture, and detection of objects in the target area and store information that users are interested in as an important basis for exercise. This article focuses on how to efficiently perform motion detection on real-time video. By introducing the mathematical model of image processing, the traditional motion detection algorithm is improved and the improved motion detection algorithm is implemented in the system. This article combines the advantages of the widely used frame difference method, target detection algorithm, and background difference method and introduces the moving object detection method combining these two algorithms. When using Gaussian mixture model for modeling, improve the parts with differences, and keep the unmatched Gaussian distribution so that the modeling effect is similar to the actual background; the binary image is obtained through the difference between frames and the threshold, and the motion change domain is extracted through mathematical morphological filtering, and finally, the moving target is detected. The experiment proved the following: when there are more motion states, the recall rate is slightly better than that of the VIBE algorithm. It decreased about 0.05 or so, but the relative accuracy rate increased by about 0.12, and the increase ratio is significantly higher than the decrease ratio. Departments need to adopt effective target extraction methods. In order to improve the accuracy of moving target detection, this paper studies the method of background model establishment and target extraction and proposes its own improvement.

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“…Combining the actual situation of sports competitions, Lu et al (2020) proposed an endpoint detection algorithm based on variance features and comprehensively designed a speech recognition model based on the Markov model. The results proved that the model was accurate and had excellent performance, providing a reference for applying artificial intelligence to sports video detection [ 12 ]. Morra et al (2020) proposed a comprehensive method to detect various complicated events in football videos starting from location data.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Detection Algorithm of Football Events in Videos

Jia

2022

Computational Intelligence and Neuroscience

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The purpose is to effectively solve the problems of high time cost, low detection accuracy, and difficult standard training samples in video processing. Based on previous investigations, football game videos are taken as research objects, and their shots are segmented to extract the keyframes. The football game videos are divided into different semantic shots using the semantic annotation method. The key events and data in the football videos are analyzed and processed using a combination of artificial rules and a genetic algorithm. Finally, the performance of the proposed model is evaluated and analyzed by using concrete example videos as data sets. Results demonstrate that adding simple artificial rules based on the classic semantic annotation algorithms can save a lot of time and costs while ensuring accuracy. The target events can be extracted and located initially using a unique lens. The model constructed by the genetic algorithm can provide higher accuracy when the training samples are insufficient. The recall and precision of events using the text detection method can reach 96.62% and 98.81%, respectively. Therefore, the proposed model has high video recognition accuracy, which can provide certain research ideas and practical experience for extracting and processing affective information in subsequent videos.

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Research on sports video detection technology motion 3D reconstruction based on hidden Markov model

Cited by 21 publications

References 22 publications

Sports Video Augmented Reality Real-Time Image Analysis of Mobile Devices

Sports Video Augmented Reality Real-Time Image Analysis of Mobile Devices

[Retracted] Detection and Adaptive Video Processing of Hyperopia Scene in Sports Video

Automatic Detection Algorithm of Football Events in Videos

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