# Survey on Videos Data Augmentation for Deep Learning Models
## 3 Review on Video Data Augmentation
### Basic Transformations
New synthetic images are generated applying local and global illumination masks to the original frames that simulate dynamical changes in illumination (spot light switch, global darkening and brightening, etc.). The data augmentation method was tested on the Stuttgart Artificial Background Subtraction dataset (SABS). Even if the dynamic light models were simplistic, an Encoder/Decoder CNN forbackground extraction improved its intersection over union (IoU)trained on the augmented dataset rather than the original one: +25% (0.3594 \(\rightarrow\) 0.6161) on IoU. In video synopsis applications, motion information is more important than video fidelity. [PERSON] et al. [43] proposed a toolbox for data augmentation able to generate synthetic surveillance videos of static cameras for video synopsis analysis. The synthetic videos are composed superimposing to an extracted background a series of coloured rectangular boxes that represent moving objects or persons. The toolbox permits to choose number, size, trajectory and speed of the boxes added to the synthetic video. In order to test the efficiency of their data augmentation method, the authors compared real camera footage from different real-world video datasets to their synthetic counterparts. When evaluated on frame compact ratio (CR), total true collision area (TCA) and total false overlapping area (FOA) metrics, the results obtained by both real-world and synthetic data were close, demonstrating the validity of the data augmentation method. In their paper, [PERSON] et al. [54] introduced AMMC (Augmentation by Mimicking Motion Change), a data augmentation strategy for object tracking that takes into consideration tracking motion features. AMMC first separates the target and background from the images. The cropped target images are transformed with operations like rotation, projection, resizing, blurring, and occlusion that reflect motion changes. The augmented target images are then superimposed on the background images at a random position in order to obtain new synthetic data. The authors trained ATOM and DiMP trackers on their simulated dataset, and they perform comprehensive experiments on five popular tracking benchmarks: LaSOT, GOT-10k, TrackingNet, OTB-100 and UAV123. In several video analysis applications (e.g., autonomous driving or surveillance), a wider field of view gives more information about the surrounding environment. For this reason, fisheye cameras are often used to capture the videos to analyse. Unfortunately, due to the high level of distortion of fisheye camera images, common data augmentation methods based on geometrical transformations cannot be directly applied. [PERSON] et al. [47] proposed a data augmentation method able to generate synthetic fisheye images from rectilinear ones. The authors defined a geometrical projection model that simulates a fisheye camera. The model possesses seven degrees of freedom (DoF): six DoFs are the relative rotation (3 DoFs) and translation (3 DoFs) between the world coordinate system and the camera coordinate system, while the last one is the focal length. The augmented data are generated randomising each DoF in order to vary translations, rotations, zooming and distortion of the simulated camera. Through a detailed ablation study, the paper evaluated the importance of each DoF calculating the mean intersection over union (mIoU) for semantic segmentation. Focal length is the most relevant, but using a 7-DoF model gives the best mIoU. However, for tests of the augmented dataset on real fisheye images, the paper gives only qualitative results.