# Survey on Videos Data Augmentation for Deep Learning Models
## 3 Review on Video Data Augmentation
### DI Models  
In [33], the authors used a double branches GAN to generate synthetic videos of a subject performing new dancing moves. The network is trained on a specific person, and it is able to generate images of that person performing moves acted by different subjects. Even if it is possible to use this system to augment an existing video dataset, one drawback is that the network needs to be retrained every time that the principal actor changes. Each synthetic frame can be generated at 12.5 fps using an NVIDIA GeForce GTX Titan Xp GPU (12 GB). A GAN is also used by [36] to augment video datasets for action recognition. For each video sequence representing an action, the generator outputs a single frame that encodes all the information regarding motion features. The generated frames and original datasets are then joined together to obtain the augmented training set. The GAN features generator can enlarge the differences between similar classes. The data augmentation model was tested on UCF101 and KTH action recognition datasets. A 2 DCNN and 3 DCNN were trained with and without data augmentation, with the data augmentation networks obtaining an increase in accuracy on both datasets with respect to the one trained on the original ones: 2 DCNN +35% on KTH and +26% on UCF101, 3 DCNN +37% on KTH and +21% on UCF101. The work of [PERSON] et al. [45] shares the idea of utilising a GAN to generate dynamic images compressing the motion information of video sequences. The authors propose a data augmentation framework that generates new synthetic dynamic images from videos using a WGAN. The augmented dataset of real and synthetic dynamic images can be used to train video classification models. In an ablation study, the proposed method was compared with other two data augmentation strategies: corner cropping with scale jittering (CCS) and horizontal flipping (HF). The three data augmentation strategies were applied separately to the LS-HMDB4 dataset and the proposed method obtained the better accuracy of 71.01% (70.28% for CCS and 69.69% for HF). More recently, [PERSON] et al. [51] presented a novel GAN based model for Appearance-Controllable Human Video Motion Transfer. The GAN model is able to generate a novel video from a source motion video and multiple target appearance videos. The innovation of their technique is the ability to control the appearance of the subject and the background in the generated synthetic videos without any retraining of the model. To achieve this result, the input are first preprocessed, extracting the skeletal poses sequence from the source motion video together with the appearance of face, upper garment and lower garment from the target appearance videos. Using the preprocessed inputs, a GAN generates a synthetic video of a new subject performing the source action. This video is then superimposed to a selected background to generate the final video sequence. [PERSON] et al. [40] designed and implemented a two-tower siamese encoder/decoder network able to synthesise, from videos of a human performing the same action taken from multiple reference viewpoints, novel videos of the same scene from different viewpoints. The network first encodes the 2D videos in a 3D volumetric latent layer. The volumetric latent layer is then used to generate the synthetic new viewpoint videos. Instead of focusing on a single subject, CrowdGAN [41] is a DL model able to recursively generate synthetic crowd videos starting from few initial context frames. The model contains two modules, one directly predicts the next frame while the other predicts the optical flow map used to warp the pixels of the starting frame. The output of the two modules is fused together to obtain the next frame. Iterating the process, using the output frames as next inputs, CrowdGAN can generate longer realistic video sequences for crowd motion analysis. In order to evaluate the applicability of the model for data augmentation, the authors tested it on two datasets for crowd counting: Mall and FDST.