Enhanced Tennis Video Analysis: R-CNN-Based Player Action Recognition and Event Detection with Dense Trajectories
Keywords:
Tennis Video Analysis, Action Recognition, Optical Flow Tracking, Scale-Invariant Feature Transform, Histogram of Directed Gradients, Motion Boundary Histogram, Ball and Player Detection, Algorithmic Accuracy in Sports Analytics.Abstract
Recognizing tennis video actions, detecting balls and players, and tracking them pose challenges due to complex backgrounds, variable lighting, and camera movements. This study presents a highly sophisticated trajectory-based system for action recognition. The system, which integrates dense optical flow tracks, scale-invariant feature transform key points, the histogram of directed gradient, optical flow, and motion boundary histogram, is a testament to the complexity and depth of our research. The system includes ball detection, tracking, and event identification in tennis videos. The aim is to automatically annotate tennis matches, enabling low-cost visual sensing equipment to record and replay matches. The approach achieves an average overall accuracy of 84.34% in tennis video classification.
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