LIJIUAN MAO
Papers
1
Total Citations
25
H-Index
1
About
Lijuan Mao is a leading researcher at the intersection of computer vision, sports analytics, and fractal artificial intelligence. Her work focuses on developing intelligent systems for real-time sports event analysis, with a particular emphasis on table tennis. Mao’s most cited paper, “Video-Based Table Tennis Tracking and Trajectory Prediction Using Convolutional Neural Networks” (2022, 25 citations), introduces a novel framework that leverages CNNs to capture and predict the unpredictable dynamics of competitive play. By integrating fractal AI principles, she addresses the challenge of modeling complex, non-linear game structures, enabling more accurate trajectory forecasting and automated event logging. This contribution is pivotal for advancing computer-aided coaching and performance analysis systems. Mao’s research demonstrates how fractal-based approaches can effectively handle the inherent chaos of sports rivalries, offering robust solutions where traditional methods falter. Her work not only pushes the boundaries of sports technology but also provides a scalable model for analyzing dynamic environments in other domains. With growing recognition, Lijuan Mao is establishing herself as a key innovator in applying deep learning and fractal theory to real-world, high-stakes scenarios.
Research Focus
Key Achievements
Top Papers
- 1