Extension of a Camera-Based Stationary Measurement Concept to Record the Position of the Motorcycle Rider Using Machine Learning
In the present master thesis, a stationary measurement concept is extended to measure the riding position of motorcyclists during cornering. The focus is the backside detection of the rider's pose using a camera with the help of deep learning. Based on this, the lean angle is calculated depending on the position of the motorcycle in space. Various deep learning models for 2D human pose estimation are examined. Through the models Mask R-CNN and HRNet, the pose of a motorcyclist is computed with little modification. The rider is represented by 15 body joints with special focus on the spine. The performance of both models is compared, and the best model is selected. The corresponding training data is recorded on a closed test area. These are labeled with a specially developed annotation tool. In combination with a license plate detector and the IPPE algorithm (Infinitesimal Plane-based Pose Estimation), which calculates the position of the motorcycle in space, the lean angle of the rider is determined. Furthermore, information such as the rider's lateral position on the motorcycle, lean-in and lean-out positions is detected. A comparison with reference data recorded on a closed test area is evaluated to review the quality of the predictions created by the model. Based on further external data sources, it is examined whether the system is deployable in real road traffic and which limitations the detection will face. With the predictions of the adapted model, the rider's lean angle and seating position can be clearly determined under certain conditions. The license plate detector does not provide reliable predictions in every scenario. Finally, possible improvements regarding training data generation, rider detection and license plate detection are mentioned and discussed.
Darmstadt, TU, Master Thesis, 2020