Figure 2.

Schematic of the data preparation and training procedure of the deep convolutional neural network (DCNN) frame classifier. Raw videos are curated and tagged on a frame-by-frame basis. Then videos are split into disjoint databases: the larger serving as the training set and the smaller serving as a validation set. The purpose of the latter is to carry out ‘early stopping’ during the training procedure. Data augmentation is performed on the training frames only. After training, the resulting frame classification model can be used for prediction on new videos.