StereoThermoLegs : label propagation with multimodal stereo cameras for automated annotation of posterior legs during running at different velocities

In sports science, thermal imaging is applied to investigate various questions related to exercise-induced stress response, muscle fatigue, anomalies, and diseases. Infrared thermography monitors thermal radiation from the skin’s surface over time. For further analysis, regions of interest are extracted and statistically analyzed. Although computer vision algorithms have grown in recent years due to data-driven approaches, this is not the case for detailed segmentation in thermal images. In a supervised manner, machine learning optimizations require a large amount of training data with input and ground truth output data. Unfortunately, obtaining annotated data are a costly problem that increases with the complexity of the task. For semantic segmentation, pixel-wise label masks must be created by experts. Few datasets meet the needs of sports scientists and physicians to perform advanced applications of thermal computer vision during physical activity and generate new insights in their felds. In this paper, a