Abstract
Drafting involves cycling so close behind another person that wind resistance is significantly reduced, which is illegal during most long distance and several short distance triathlon and duathlon events. In this paper, a proof of concept for a drafting detection system based on computer vision is proposed. After detecting and tracking a bicycle through the various scenes, the distance to this object is estimated through computational geometry. The probability of drafting is then determined through statistical analysis of subsequent measurements over an extended period of time. These algorithms are tested using a static recording and a recording that simulates a race situation with ground truth distances obtained from a Light Detection And Ranging (LiDAR) system. The most accurate developed distance estimation method yields an average error of m in our test scenario. When sampling the distances at periods of 1 or 2 s, simulations demonstrate that a drafting violation is detected quickly for cyclists riding at 2 m or more below the limit, while generally avoiding false positives during the race-like test set-up and five hour race simulations.
Highlights
As in most endurance sports, the goal of triathlon races is to reach the finish line as quickly as possible.In addition to optimizing training, rest, and nutrition, triathletes can greatly improve their performance by working on a streamlined posture
We proposed two strategies to estimate the distance between two bicycles: the Height-Based method (HHm) and the Wheel Position-Based method (WPm)
The HHm produces a lower overall absolute distance error. Note that these measurements have been realized with a known tilt angle; the tilt angle deviation is assumed to be close to zero for the wheel-based method. The limitation of this static situation test is that it is considerably different from what happens during a triathlon race, since only one, static athlete is visible at any given time
Summary
As in most endurance sports, the goal of triathlon races is to reach the finish line as quickly as possible. A drafting detection system based on video taken from a camera mounted under the saddle of a bicycle, equipped with computer vision techniques can offer a solution to these problems. We trained, applied, and analyzed the performance of real-time CNN-based object detector, for detecting race, time trial, and triathlon bicycles. We developed an efficient method which determines the probability of violating the drafting rule, based on successive distance estimations and a model of the measurement error. The behavior of this method is rigorously tested in a realistic scenario and through the use of simulations
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