Analysis of Competition and Training Videos of Speed Climbing Athletes Using Feature and Human Body Keypoint Detection Algorithms.

Compared to 25 years ago, the climbing sport itself has changed dramatically. From a rock climbing modification to a separation in three independent disciplines, the requirements to athletes and trainers increased rapidly. To ensure continuous improvement of the sport itself, the usage of measurement and sensor technology is unavoidable. Especially in the field of the discipline speed climbing, which will be performed as a single discipline at the Olympic Games 2024 in Paris, the current state of the art of movement analysis only consists of video analysis and the benefit of the experience of trainers. Therefore, this paper presents a novel method, which supports trainers and athletes and enables analysis of motion sequences and techniques. Prerecorded video footage is combined with existing feature and human body keypoint detection algorithms and standardized boundary conditions. Therefore, several image processing steps are necessary to convert the recorded movement of different speed climbing athletes to significant parameters for detailed analysis. By studying climbing trials of professional athletes and the used techniques in different sections of the speed climbing wall, the aim among others is to get comparable results and detect mistakes. As a conclusion, the presented method enables powerful analysis of speed climbing training and competition and serves with the aid of a user-friendly designed interface as a support for trainers and athletes for the evaluation of motion sequences.

Methodology for the Automated Visual Detection of Bird and Bat Collision Fatalities at Onshore Wind Turbines.

The number of collision fatalities is one of the main quantification measures for research concerning wind power impacts on birds and bats. Despite being integral in ongoing investigations as well as regulatory approvals, the state-of-the-art method for the detection of fatalities remains a manual search by humans or dogs. This is expensive, time consuming and the efficiency varies greatly among different studies. Therefore, we developed a methodology for the automatic detection using visual/near-infrared cameras for daytime and thermal cameras for nighttime. The cameras can be installed in the nacelle of wind turbines and monitor the area below. The methodology is centered around software that analyzes the images in real time using pixel-wise and region-based methods. We found that the structural similarity is the most important measure for the decision about a detection. Phantom drop tests in the actual wind test field with the system installed on 75 m above the ground resulted in a sensitivity of 75.6% for the nighttime detection and 84.3% for the daylight detection. The night camera detected 2.47 false positives per hour using a time window designed for our phantom drop tests. However, in real applications this time window can be extended to eliminate false positives caused by nightly active animals. Excluding these from our data reduced the false positive rate to 0.05. The daylight camera detected 0.20 false positives per hour. Our proposed method has the advantages of being more consistent, more objective, less time consuming, and less expensive than manual search methods.

Collision risk of bats with small wind turbines: Worst-case scenarios near roosts, commuting and hunting structures.

Small wind turbines (SWTs) have become increasingly common within the last decade, but their impact on wildlife, especially bats, is largely unknown. We conducted an operational experiment by sequentially placing a mobile SWT with five different operational modes at six sites of high bat activity, including roosts, commuting structures, and highly frequented hunting areas. Bat flight trajectories around the SWT were documented at each site during five consecutive nights using a specifically designed high-spatial-resolution 3D camera. The recordings showed high bat activity levels close to the SWT (7,065 flight trajectories within a 10-m radius). The minimum distance to the rotor of each trajectory varied between 0 and 18 m, with a mean of 4.6 m across all sites. Linear mixed models created to account for site differences showed that, compared to a reference pole without a SWT, bats flew 0.4 m closer to the rotor (95% CI 0.3-0.6 m) if it was out of operation and 0.3 m closer (95% CI 0.1-0.4 m) if it was moving slowly. Exploratory behavior was frequently observed, with many bats deviating from their original flight trajectory to approach the rotor. Among 7,850 documented trajectories, 176 crossed the rotor, including 65 while it was in motion. The collision of one P. pygmaeus individual occurred during the experiment. These results demonstrate that, despite the generally strong ability of bats to evade moving rotor blades, bat casualties at SWTs placed at sites of high bat activity can reach or exceed the current threshold levels set for large wind turbines. As SWTs provide less energy than large turbines, their negative impact on bats should be minimized by avoidance measures such as a bat-friendly site selection or curtailment algorithms.