Basketball Tactics Analysis Based on Improved Openpose Algorithm and Its Application

The use of information technology has currently resulted in a change in people's production and lifestyles as well as a catalyst for the sports industry's evolution. Currently, there is a growing use of digitization in the sports industry's basketball realm. Using 2D posture estimatio...

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Bibliographic Details
Published in:Journal of Electrical Systems Vol. 20; no. 3s; pp. 374 - 383
Main Author: Xu, Jingsheng
Format: Journal Article
Language:English
Published: Paris Engineering and Scientific Research Groups 04.04.2024
Subjects:
Algorithms
Basketball
Classification
Feature extraction
Identification methods
Kinematics
Machine learning
Motion perception
Multidimensional data
Pose estimation
Real time
Semantics
Sports
Video
ISSN:1112-5209
Online Access:Get full text
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Summary:The use of information technology has currently resulted in a change in people's production and lifestyles as well as a catalyst for the sports industry's evolution. Currently, there is a growing use of digitization in the sports industry's basketball realm. Using 2D posture estimation, a novel lightweight deep learning architecture (LDLA) is built to accomplish the automatic analysis of basketball game footage. We offer a real-time method to identify numerous people's 2D poses in a video. Additionally, it examines how 2D pose estimation might be used to analyse basketball shooting videos. First, group and global motion features are extracted to represent semantic events. A full basketball game video is broken down into three stages: clip-based segmentation, classification of semantics occurrences utilizing audio and video, and temporal sequence characteristics employing GRU CNN. Video is first processed to create some basic video categorization and segmentation using the visual, movement, and auditory data. Further use of domain knowledge is made to find important events in the basketball video. Shot and image threshold recognition algorithms for videos have used both optical and kinematic prediction information; scene classification comes next. The placements of probable semantic events, such as "fouling" and "shooting at the basket," are then discovered by comparing the multidimensional data with supplementary domain expertise. Experimental results demonstrate the proposed LDLA method achieves 99.6% of accuracy, 93.2% of precision, 93.5% of recall and 89.5% of Fl-score.
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ISSN:1112-5209
DOI:10.52783/jes.1303