# Survey on Videos Data Augmentation for Deep Learning Models
## 1 Introduction
In this case, the input of the DL models is not single images but streams of multiple images with temporal and spatial correlation between each others. While some of the models meant for image analysis can be used out of the box to analyse videos, usually some changes have to be done to take into account the temporal dimension. Optical flow [15], 3D convolutions [16] and Recurrent Neural Networks (RNN) [17; 18] are the most common methods used to handle image sequences. However, the correlation in time and space in between images of the same sequence needs to be taken into account not only in the design of the DL models, but also in the design of the datasets. Geometric and color space transformation can usually be applied to videos keeping them constant for the entire image sequence, but, for more complex methods, the changes need to be more significant. In generative modeling, the Generator network needs to keep some information of the past frames. The DL models used to analyse image sequences (Optical flow, 3D convolutions and RNN) are a proper solution. In simulation, the physical interaction between objects needs to be taken into account. If the focus is in human action recognition or prediction, the skeletal animation of the subjects is needed to simulate the motion. In domain randomization methods, camera motion must be taken into account and the variation in textures, illumination and objects shapes must be constant or coherent through the entire video sequence. Reviews on image data augmentation for DL models have already been published [19; 20; 21; 22; 23]. In their paper, [PERSON] et al. [19] realised a complete survey on image data augmentation for DL, covering both basic image manipulation and DL approaches. The basic manipulation approaches are composed of geometrical and color space transformations, kernel filters, noise injection, mixing images and random erasing. In their review, the authors focus also on more recent DL approaches: feature space augmentation, adversarial training, GAN-based and Neural Style Transfer. More recently, [PERSON] et al. [20] grouped the papers of their review on image data augmentation in a similar fashion. In addition, the authors present an analysis of the state of the art specific to different application domains. On the contrary, [PERSON] et al. [21] focused their review on the more specific problem of face data augmentation. The authors reviewed the state of the art on face data augmentation under several points of view: transformation types, transformation methods, and evaluation metrics. In face data augmentation, likewise for general image data augmentation, generative models have recently been the primary choice, replacing or enhancing most of the other methods. A different application area was tackled by [PERSON] et al. [22] in their review of medical image data augmentation. The paper analyses the state of the art of CT and MRI image data augmentation for DL applications. The reviewed works were divided into three groups. The first group are basic augmentation techniques that correspond to geometric and color space transformations. The second group are deformable augmentation techniques, consisting of scaling and warping masks applied to the original images. The last group are the DL augmentation techniques, further divided into GAN-based and others. The author pointed out that basic and deformable methods are still more popular being easy to apply on medical data and due to the availability of several software libraries for their implementation. On the other hand, DL methods are evolving rapidly and will gain more popularity given their ability to tackle complex data synthesis problems such as cross-domain image synthesis. Image mixing and deleting data augmentation strategies are reviewed by [PERSON] in his survey [23]. The reviewed papers are split into three categories: erasing image patches; cut image regions and replacing them with patches from other images; and mixing multiple images.