# Survey on Videos Data Augmentation for Deep Learning Models
## 3 Review on Video Data Augmentation
### Simulation
The dataset provides video frames, camera matrices, depth data, instance level segmentation, instance level amodal segmentation and the corresponding 3D object shapes. No quantitative experiments on real-world datasets were performed. [PERSON] et al. [55], on the other hand, created their own rendering engine (NOVA) based on Unity to allow researchers with no experience in computer graphics to generate high quality datasets with accurate and dense annotations. The authors collected a real-world dataset named PTAW172 Real, and they used NOVA to generate a synthetic one called PTAW217 Synnt. They tested several state-of-the-art person trackers (DiMP, ATOM, KYS and PrDiM) training them on both PTAW172 Real and PTAW217 Synnt. Overall, the models trained on the synthetic dataset obtained better IoU results compared to the same models trained on PTAW172 Real. Surveillance video analysis is another area of application of simulated video data augmentation. In this case, the synthetic data need to be generated based on an accurate crowd and traffic simulation. The area to be watched can be wide, as in aerial surveillance imagery. Virtual flight simulators can come in handy to render photo realistic videos of wide areas. [PERSON] et al. [29] proposed a system for the generation of synthetic wide area aerial surveillance imagery. The authors simulated the traffic in an urban environment using Matlab and a traffic simulator (SUMO). The 3D models of city buildings were extruded from Open Street Map data and exported, together with the traffic simulations, to X-Plane flight simulator, which has been used as the main visualization tool. The system was able to generate simulated aerial videos of wide urban areas. With the rise of autonomous vehicles, one of the most relevant applications for computer vision and DL are self driving cars. [PERSON] et al. [32] explore using commercial video games to generate large-scale, high-fidelity training data for semantic segmentation in autonomous driving scenarios. The authors use GTAV to simulate, render and annotate a synthetic dataset of cars driving in urban roads (URSA dataset). In-game AI is used to drive the vehicles in simulation and an optimised annotation algorithm is proposed to segment the synthetic frames. The performances of URSA dataset used as a traning set for semantic segmentation were tested on the popular CityScapes dataset. The evaluation metric used was the class-specific intersection over union (c-IoU) averaged over all of the 19 classes of the dataset, and the networks used for segmentation were FCN and SegNet. The FCN model trained on an URSA synthetic dataset obtained an IoU in line with other state-of-the-art synthetic datasets: URSA = 0.139; Playing For Benchmarks (PFB) = 0.170; Synthia = 0.126. The performances are still far from the one obtained training directly FCN on CityScapes (c-IoU 0.449). However, using URSA plus only 10% of CityScapes dataset for training, the mean c-IoU gets closer to the baseline (0.422). Autonomous drones have now become very popular. [PERSON] et al. [35] used Unreal Engine and the AirSim plugin to generate a simulated dataset of flying drones. The authors simulated several sensors other than RGB cameras (GPS, depth sensor, accelerometer and gyroscope), and they were able to produce several pieces of simulated data (RGB on-board camera images, and depth, normal and semantic segmentation maps). Different weather conditions and seasonal changes were present. In problems where camera motion is the central focus, 3D simulated data are a good solution for data augmentation. [PERSON] et al. [30] presented a framework to generate synthetic shaking videos to train video stabilization and deblurring models. As seen in previous papers, the authors generate various camera paths and motions starting from motion capture data. Matlab was used to process the original shaking motion capture data and produce the new synthetic camera motions.