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In this study, we compared a monocular computer vision (MCV)-based approach with the golden standard for collecting kinematic data on ski tracks (i.e., video-based stereophotogrammetry) and assessed its deployment readiness for answering applied research questions in the context of alpine skiing. The investigated MCV-based approach predicted the three-dimensional human pose and ski orientation based on the image data from a single camera. The data set used for training and testing the underlying deep nets originated from a field experiment with six competitive alpine skiers. The normalized mean per joint position error of the MVC-based approach was found to be 0.08 ± 0.01 m. Knee flexion showed an accuracy and precision (in parenthesis) of 0.4 ± 7.1° (7.2 ± 1.5°) for the outside leg, and −0.2 ± 5.0° (6.7 ± 1.1°) for the inside leg. For hip flexion, the corresponding values were −0.4 ± 6.1° (4.4° ± 1.5°) and −0.7 ± 4.7° (3.7 ± 1.0°), respectively. The accuracy and precision of skiing-related metrics were revealed to be 0.03 ± 0.01 m (0.01 ± 0.00 m) for relative center of mass position, −0.1 ± 3.8° (3.4 ± 0.9) for lean angle, 0.01 ± 0.03 m (0.02 ± 0.01 m) for center of mass to outside ankle distance, 0.01 ± 0.05 m (0.03 ± 0.01 m) for fore/aft position, and 0.00 ± 0.01 m2 (0.01 ± 0.00 m2) for drag area. Such magnitudes can be considered acceptable for detecting relevant differences in the context of alpine skiing.

Contemporary camera-equipped wearable devices, such as smartphones and tablets, are now powerful enough to be used as ubiquitous remote sources of live video streams. However, as these devices may operate in various positions, including upside down or rotated, encoding the captured video into a data format suitable for live streaming also includes the rotation of video frames to compensate the device rotation effect.In this paper, we analyze the impact of the video rotation procedures to the frame rate of the produced live video. MJPEG is used as a video encoding format. We compare the performance of two video rotation methods: classical pixel-level rotation based on rotation matrix and JPEG tagging using EXIF metadata. The results show that EXIF-based tagging outperforms matrix-based calculations significantly, and gets superior as video resolution increases. However, since EXIF metadata are not yet widely supported in modern web browsers, if compatibility with open user community is required, matrix-based rotation is the option to go with.