# Survey on Videos Data Augmentation for Deep Learning Models
## 1 Introduction  
We live in a world where most of our actions are constantly captured by cameras. Video cameras are spread almost everywhere: in smartphones, computers, drones, surveillance systems, cars, robots, intercoms, etc. Image Processing (IP) and Computer Vision (CV) models, able to extract and analyse information from images, are becoming more and more important. With the advent of Deep Learning (DL) and the increase in computational power, classical CV algorithms are quickly being replaced by Convolutional Neural Networks (CNN) or other DL models [1, 2]. Typically, DL models possess a huge number of parameters that need to be trained. The risk of overfitting with such big models is very high and big datasets with high variability are needed for networks to be able to generalise. Unfortunately, collecting a big collection of images or videos and labelling them is both resource and time consuming, and, in some cases, even impossible. In medical image analysis, data such as computerized tomography (CT) and magnetic resonance imaging (MRI) scans are expensive and time consuming to collect. Moreover, medical data are protected by strict privacy protocols, making it difficult to obtain past recordings from hospitals. In robotics, a prolonged operation of robots for collecting data can result in the wearing or damaging of components, labour intensive procedures and dangerous interactions between machines and operators. Collecting data for autonomous vehicles control have similar problems. Data collection in this case consists of running a vehicle (car, drone, boat) with a camera mounted on top in various environmental conditions (weather, time of the day, city versus countryside, etc.). This process can take a conspicuous amount of time, it is expensive, the vehicle can be damaged and special permissions to operate in restricted areas are often needed. From these examples, it is clear how data collection can become a complex and troublesome process, but it is only part of the problem. In order to generate a dataset for supervised learning models, data need to be labelled. In many occasions, the labelling process cannot be automatised, and each image needs to be labelled manually by humans (e.g., medical images segmentation). The consequence of the aforementioned problems in data collection and labelling is the generation of small and unbalanced datasets. Several techniques exist to tone down this problem, reducing the overfitting and improving the generalisation capabilities of the models. For some problems like object recognition, face recognition and autonomous driving, big generic and public datasets have already been collected [3; 4; 5; 6]. Pretraining is a technique where models are first trained on big existing datasets built for more generic tasks. In this way, pretrained models can learn a base knowledge to be transferred to a specific problem. A pretrained model is able to converge faster on a new dataset, needing less data [7]. A similar approach is Transfer Learning: models pretrained on a dataset for a specific data distribution are able to transfer part of the acquired knowledge to a different distribution with small or no fine-tuning. Data regularization techniques (Dropout and Batch normalization) are other approaches to reduce overfitting. Using a combination of these techniques, tasks where data are scarce can be more easily handled by DL models. However, none of the previous methods directly solve the problems of shortage of data and unbalanced datasets. Data augmentation techniques, on the other hand, address the lack of data artificially generating new ones. The most basic technique of data augmentation for image analysis is noise injection: the dataset is expanded creating duplicates of the original images injected of random values in the RGB space. Since the introduction of AlexNet in 2012 [8], geometric and color space transformations are common data augmentation techniques used to improve the performance of DL models for image analysis.