  The immense popularity of racket sports has fueled substantial demand in
tactical analysis with broadcast videos. However, existing manual methods
require laborious annotation, and recent attempts leveraging video perception
models are limited to low-level annotations like ball trajectories, overlooking
tactics that necessitate an understanding of stroke techniques.
State-of-the-art action segmentation models also struggle with technique
recognition due to frequent occlusions and motion-induced blurring in racket
sports videos. To address these challenges, We propose ViSTec, a Video-based
Sports Technique recognition model inspired by human cognition that synergizes
sparse visual data with rich contextual insights. Our approach integrates a
graph to explicitly model strategic knowledge in stroke sequences and enhance
technique recognition with contextual inductive bias. A two-stage action
perception model is jointly trained to align with the contextual knowledge in
the graph. Experiments demonstrate that our method outperforms existing models
by a significant margin. Case studies with experts from the Chinese national
table tennis team validate our model's capacity to automate analysis for
technical actions and tactical strategies. More details are available at:
https://ViSTec2024.github.io/.
