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Purpose: To assess injury risk in elite-level youth football players based on anthropometric, motor coordination and physical performance measures with a machine learning approach.Methods: A total of 734 players in the U10 to U15 age categories (mean age: 11.7 +/-1.7 years) from seven Belgian youth academies were prospectively followed during one season. Football exposure and occurring injuries were monitored continuously by the academies' coaching and medical staff, respectively. Preseason anthropometric measurements (height, weight, and sitting height) were taken and test batteries to assess motor coordination and physical fitness (strength, flexibility, speed, agility, and endurance) were performed. An extreme gradient boosting algorithms (XGBoost) was used to predict injury based on the preseason test results.Subsequently, the same approach was used to classify injuries as either overuse or acute.Results: During the season, half of the players (n = 368) sustained at least one injury. Of the first occurring injuries, 173 were identified as overuse and 195 as acute injuries. The machine learning algorithm was able to identify the injured players in the hold-out test sample with 85% precision, 85% recall (sensitivity) and 85% accuracy (f1-score). Furthermore, injuries could be classified as overuse or acute with 78% precision, 78% recall and 78% accuracy. Conclusion:Our machine learning algorithm was able to predict injury and to distinguish overuse from acute injuries with reasonably high accuracy based on preseason measures. Hence, it is a promising approach to assess injury risk among elite-level youth football players. This new

This is a review article describing the recent developments in Video based Fire Detection (VFD). Video surveillance cameras and computer vision methods are widely used in many security applications. It is also possible to use security cameras and special purpose infrared surveillance cameras for fire detection. This requires intelligent video processing techniques for detection and analysis of uncontrolled fire behavior. VFD may help reduce the detection time compared to the currently available sensors in both indoors and outdoors because cameras can monitor "volumes" and do not have transport delay that the traditional "point" sensors suffer from. It is possible to cover an area of 100 km2 using a single pan-tilt-zoom camera placed on a hilltop for wildfire detection. Another benefit of the VFD systems is that they can provide crucial information about the size and growth of the fire, direction of smoke propagation.