Fully Automatic Camera for Personalized Highlight Generation in Sporting Events.

Personally curated content in short-form video formats provides added value for participants and spectators but is often disregarded in lower-level events because it is too labor-intensive to create or is not recorded at all. Our smart sensor-driven tripod focuses on supplying a unified sensor and video solution to capture personalized highlights for participants in various sporting events with low computational and hardware costs. The relevant parts of the video for each participant are automatically determined by using the timestamps of his/her received sensor data. This is achieved through a customizable clipping mechanism that processes and optimizes both video and sensor data. The clipping mechanism is driven by sensing nearby signals of Adaptive Network Topology (ANT+) capable devices worn by the athletes that provide both locality information and identification. The device was deployed and tested in an amateur-level cycling race in which it provided clips with a detection rate of 92.9%. The associated sensor data were used to automatically extract peloton passages and report riders' positions on the course, as well as which participants were grouped together. Insights derived from sensor signals can be processed and published in real time, and an upload optimization scheme is proposed that can provide video clips for each rider a maximum of 5 min after the passage if video upload is enabled.

A machine learning approach for the classification of sports based on a coaches' perspective of environmental, individual and task requirements: A sports profile analysis.

Well-designed talent programmes in sports with a focus on talent identification, orientation, development, and transfer support the engagement of young individuals and the pursuit of elite performance. To facilitate these processes, an analysis of task, environmental and individual characteristics per sport is much needed. The aims of this study were to 1) analyse whether unique profiles per sport could be established by generic characteristics and 2) to discuss similarities and differences for the potential application in talent development and transfer. By means of a validated survey, 1247 coaches from 34 sports ranked 18 characteristics on importance to their sports (0 = not important - 10 = very important). To discriminate the responses per sport a Discriminant Analysis (DA) was carried out. To refine the DA-classification, Uniform Manifold Approximation and Projection (UMAP) with CatBoost classifier was performed. To test the performance of the CatBoost classifier-algorithm, a confusion-matrix was generated. The cross-validated DA showed that 70.2% of the coaches were correctly classified to their sport. The UMAP/CatBoost technique revealed 75.1% accuracy with correctly predicted responses per sport ranging from 18.2% (sailing) to 98.2% (soccer). With varying precision, the algorithm was able to differentiate sports by importance of its characteristics indicating similarities and differences per sport.

Video-Based Analysis and Reporting of Riding Behavior in Cyclocross Segments.

Video-based trajectory analysis might be rather well discussed in sports, such as soccer or basketball, but in cycling, this is far less common. In this paper, a video processing pipeline to extract riding lines in cyclocross races is presented. The pipeline consists of a stepwise analysis process to extract riding behavior from a region (i.e., the fence) in a video camera feed. In the first step, the riders are identified by an Alphapose skeleton detector and tracked with a spatiotemporally aware pose tracker. Next, each detected pose is enriched with additional meta-information, such as rider modus (e.g., sitting on the saddle or standing on the pedals) and detected team (based on the worn jerseys). Finally, a post-processor brings all the information together and proposes ride lines with meta-information for the riders in the fence. The presented methodology can provide interesting insights, such as intra-athlete ride line clustering, anomaly detection, and detailed breakdowns of riding and running durations within the segment. Such detailed rider info can be very valuable for performance analysis, storytelling, and automatic summarization.

Visibility of protective helmets worn by forestry workers.

BACKGROUND: High visibility helmets must be worn by forestry workers in New Zealand for protection and as conspicuous 'clothing' to alert workers to the presence and location of other workers. The colours yellow-green (fluorescent yellow-green) and 'watermelon' (fluorescent pink) are used and both appear to be conspicuous. To solve a controversy, we investigated which helmet colour is more visible for use in a forest setting for workers having normal or defective colour vision. METHOD: We obtained threshold angular sizes for two-millimetre square samples of helmet material presented against a textured background containing colours representative of those found in the foliage and bark of the most common forest type (Pinus Radiata). Observers with normal colour vision (n = 22) and with deutan (n = 8) and protan (n = 6) defects participated. Subjects with mild colour vision defects were excluded. RESULTS: The yellow-green colour was significantly more visible than the pink for the normal (p +/- 0.001) and protan (p +/- 0.05) observers. For the deutan observers the pink helmet colour was significantly more visible (p +/- 0.01). The median equivalent outdoor detection distances were for normal observers 400 m (pink) and 500 m (yellow-green); for protan observers 185 m (pink) and 500 m (yellow-green); and for deutan observers 550 m (pink) and 450 m (yellow-green). CONCLUSIONS: The yellow-green helmet can be detected at large distances by all observers. The yellow-green helmet has greater reflectance and therefore greater luminance contrast. The pink helmet colour can be confused with green forest background colours by observers with protan defects. For some observers with a protan colour vision defect, detection distances for the pink helmet colour are less than half of normal detection distances.