**5. Object detection**

The task of object detection is to find instances of real-world objects in images or videos. A detected object is typically marked with a bounding box and labeled with a corresponding class label and classification confidence value. Thus, object detection includes both the problems of finding the location of the object on the scene and of classification for predicting the class to which the object belongs to.

In case of player detection, the object detector should be able to overcome challenging conditions such as variable number of players, different player positions, varying distance of the player from the camera, the possibility of changing shape and appearance of players in time, presence of the blur of due to the speed of the movement, occlusion, shadows of artificial and external light, as well as cluttered background.

Nowadays, the focus in object detection is on CNNs that have been extended to be able to both detect and localize individual objects on the scene. In the following subsections, two different object detectors YOLO and Mask R-CNN are described with a corresponding experiment of player and ball detection.
