Deep Learning in Motion Analysis for False Start Detection in Speedway Racing
Abstract
Accurately identifying false starts in speedway racing is a very challenging task due to the subtle nature of pre-start movements. Manual detection methods, often dependent on the judgment of race officials, are prone to errors and subjectivity, leading to inconsistencies in decision-making. This paper introduces an automated approach that leverages computer vision methods to enhance detection precision. Here, we have expanded its use to detect false starts in speedway racing. The proposed approach introduces image processing techniques with 3D Convolutional Neural Networks (CNNs) and Long-Short-Term Memory (LSTM) networks to analyze rider movements during the starting procedure. Unlike manual detection, which often misses fine movements at the start line, our method uses 3D CNNs to monitor racer movements and applies LSTM networks to assess time-based motion patterns that signal false starts. The presented results show that the 3D CNN achieved an accuracy of 86.36% with a higher precision when compared to traditional methods. This automated process not only enhances fairness in competitive racing, but also illustrates the broader capability of emerging technologies to refine decision-making in sports.
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