Deep Learning-Based Football Player Detection in Videos

Figure 2

Model architecture of the football player detection system. First, an input image went through five consecutive convolutional layers with batch normalization and leaky ReLU as the activation function in order to extract features with different levels of spatial resolution. Then an upsampled feature map were combined with the feature level from the lower level to obtain the player confidence map and the corresponding player bounding box.