# Survey on Videos Data Augmentation for Deep Learning Models
## 1 Introduction
Cropping, flipping, rotating, translating, histogram and RGB values alteration all fall in this category. With the improvements in Neural Networks (NN) and DL, more advanced data augmentation methods increased. Strategies based on generative modeling are able to generate new input images belonging to a similar distribution of the original dataset. These strategies use Generative adversarial networks (GANs) to generate the new images [9]. A GAN consists of two networks, a generator and a discriminator that compete against each other during training: the generator tries to produce an image belonging to a distribution of interest from input images, while the discriminator tries to distinguish generated images from the ones belonging to the true data distribution. After training, the generator can be used to augment the original dataset with newly generated images from the same distribution of the original dataset. Neural Style Transfer is another DL based methodology able to augment the size of image datasets. The idea is to alter the latent space of an Encoder/Decoder CNN in order to generate images with different styles. The output image of the Decoder is similar to the input one but with a difference in style that depends on the changes applied to the latent layer. In [10], the authors propose a GAN architecture, based on Neural Style Transfer, able to generate photo realistic images that can be used to create synthetic datasets. We already mentioned several data augmentation techniques to improve training and reduce overfitting of image processing DL models. A common trait of all these methodologies is the use of images from the original dataset as a base for generating the new images. A different approach is to generate the images for the augmented dataset from physical models that approximate the world. In this case, detailed models of the environment, the physics and the cameras are defined by the researcher and used to generate synthetic approximation of real images. The researchers are able to randomise the scene and create varied simulated images tuning the models' parameters. This ability to tune the models offers a higher flexibility compared to other data augmentation techniques. Graphical and physical simulators are powerful tools and several researchers are using them for the generation of artificial datasets [11]. This process has been accelerated by the advancement in the video games industry. Modern game engines (Unity [12] and Unreal Engine [13] among others) are able to render in a few milliseconds photo-realistic images, simulate realistic physical interaction between objects, and they offer powerful scripting and designing tools to recreate detailed artificial scenes. Exploiting game engines, researchers can create varied and faithful to reality synthetic datasets to train large DLmodels for image analysis. [PERSON] et al. [11] used Unreal Engine to create a photo realistic synthetic dataset for training an object detection model. The dataset was generated randomising objects position, scene, illumination and camera position. However, photo realism is not always the way to go to create a synthetic dataset. Domain Randomization (DR) is a method to generate synthetic data in simulation [14]. If the variability in the generated dataset is significantly high, models trained on it will be able to generalise to the real world. In order to achieve this result, the scene parameters need to be randomized to an extent that the generated images can fully cover the desired data distribution. In this way, to overtake the reality gap (intrinsic difference between simulated and real worlds), simulated datasets are not generated to be as realistic as possible, but to contain a high variability in lighting, object shapes, textures, camera position, and physics behaviours. Video analysis adds the temporal dimension to the images problem, resulting in a very complex challenge. With the introduction of industry 4.0, robotics and autonomous vehicles, video analysis is becoming a focal problem for the research community.