Abstract
Video-based trajectory analysis might be rather well discussed in sports, such as soccer or basketball, but in cycling, this is far less common. In this paper, a video processing pipeline to extract riding lines in cyclocross races is presented. The pipeline consists of a stepwise analysis process to extract riding behavior from a region (i.e., the fence) in a video camera feed. In the first step, the riders are identified by an Alphapose skeleton detector and tracked with a spatiotemporally aware pose tracker. Next, each detected pose is enriched with additional meta-information, such as rider modus (e.g., sitting on the saddle or standing on the pedals) and detected team (based on the worn jerseys). Finally, a post-processor brings all the information together and proposes ride lines with meta-information for the riders in the fence. The presented methodology can provide interesting insights, such as intra-athlete ride line clustering, anomaly detection, and detailed breakdowns of riding and running durations within the segment. Such detailed rider info can be very valuable for performance analysis, storytelling, and automatic summarization.
Highlights
IDLab, Ghent University—IMEC, Technologiepark-Zwijnaarde 122, 9052 Ghent, Belgium; Abstract: Video-based trajectory analysis might be rather well discussed in sports, such as soccer or basketball, but in cycling, this is far less common
Wout Van Aert and Filippo Ganna, but it is about the impact of that difference in power combined with a particular context and considering all other available data that tell the real story and help to understand the race dynamics
In last year’s Tour de France, for instance, NTT showcased the potential of big data in cycling by recording more than 35 million records per stage [3]. This raw data on its own is of limited practicability and relevance for fans and sports stakeholders
Summary
IDLab, Ghent University—IMEC, Technologiepark-Zwijnaarde 122, 9052 Ghent, Belgium; Abstract: Video-based trajectory analysis might be rather well discussed in sports, such as soccer or basketball, but in cycling, this is far less common. The presented methodology can provide interesting insights, such as intra-athlete ride line clustering, anomaly detection, and detailed breakdowns of riding and running durations within the segment Such detailed rider info can be very valuable for performance analysis, storytelling, and automatic summarization. The real goal of sports-related storytelling is to really understand what the raw sensor values mean, using a unique mix of feature engineering and machine learning It is not about identifying the power or speed difference between. In last year’s Tour de France, for instance, NTT showcased the potential of big data in cycling by recording more than 35 million records per stage [3] This raw data on its own is of limited practicability and relevance for fans and sports stakeholders. Lap times in sport events are already very valuable and Academic Editors: Franck Multon, Andrea Mannini and Adnane Boukhayma
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