A comparative study on multi-object tracking methods for sports events
Sung-Won Moon, Jiwon Lee, Do-Won Nam, Howon Kim, Wonjun Kim · 2017
Due to the rapid growth of machine learning technology, there is a need for research to automatically recognize objects and analyze their behavior in various fields, as is the case with sports. Currently, a system for detecting and tracking multiple objects in a sporting event is not accurate enough. Since most of the services depend on the manual operation of an experienced operator, it is necessary to develop a real time tracking technique for detecting the position of an object. In this paper, we propose an algorithm for multi - object tracking in a sporting event by presenting the results of comparing the performance of existing algorithms for multiobject tracking.