*Application of Deep Learning Methods for Detection and Tracking of Players DOI: http://dx.doi.org/10.5772/intechopen.96308*

The second model (YBP) was trained using transfer learning, on PBD-Handball part of the dataset. The input image resolution was increased to 1024 x 1024 from 608 x 608 of the original model and the model was trained for approximately 80 epochs. **Figure 4** shows an example of detection results for the "person" class.

To evaluate the performance of a model, the average precision (AP) metric of both classes and the mean average precision are used and shown on **Table 1**.

The best results for ball detection in terms of AP were achieved with the YPB model, which was trained on additional examples for both ball and person class and had an increased input image size. A small amount of training data can significantly improve detection results as can be seen in the example of ball detection which improved for 23%. The achieved results are satisfactory given the demanding environment but are not sufficient for commercial application, so the training dataset should be increased.
