Real-Time Speed Detection in Sports Events Using Cloud-Sensor Fusion with Support Vector Machines

This research presents an innovative approach to monitoring sports events for real-time speed detection using Support Vector Machines (SVM) and cloud-sensor fusion technologies. The suggested solution takes use of cloud computing to efficiently process and analyze data by combining feeds from many sensors placed strategically around the sports stadium. The effectiveness of this method is shown by practical studies carried out in real-life sporting contexts, particularly in relation to the monitoring of athletes' speeds. In comparison to more traditional methods, the results show our approach significantly improves calculation speed and accuracy. A strong option can reliably identify speeds in the dynamic and fast-paced environment of athletic events is the combination of sensor data in the cloud with SVM -based classification algorithms. This development shows potential for several uses, such as optimizing training, real-time event streaming, and performance assessment. It provides a complex but realistic framework for detecting sports speeds in real-time, which improves viewers' understanding of athletes' performances and the quality of the games they watch.