# Survey on Videos Data Augmentation for Deep Learning Models
## 3 Review on Video Data Augmentation
### Basic Transformations  
A simple technique for temporal data augmentation in videos was proposed in [26]. The paper focuses on the problem of action recognition from videos. The authors augment the training set for their model applying iteratively a temporal cropping several times to each original video sequence. They temporally sub-sampled each video sequence of length \(l\) with a stride \(s\), obtaining \(s\) new sequences of length \(l/s\). A three-stream CNN was trained with and without data augmentation. The accuracy of both networks was evaluated on four different datasets: UCF101, HMDB51, Hollywood2 and Youtube. The network trained with data augmentation improved the accuracy on all the datasets (+1.3% on UCF101, +1.1% on HMDB51, +1.2% on Hollywood2 and +2.5% on Youtube). Data augmentation using temporal cropping is proposed also by [PERSON] et al. [38]. The authors augment a video dataset of hand gestures splitting the original 12 frames videos in 3 videos of 8 frames each (1 st to 8 th, 3 rd to 10 th and the 5 th to 12 th frame). They also invert the temporal order of the frames obtaining an augmented dataset six times larger than the original. The proposed data augmentation strategy was used to augment the VIVA dataset. Their mdCNN trained on the augmented dataset improved the accuracy of 6% over the same network trained without data augmentation. Applying commonly used image-level data augmentation strategies to video sequences may introduce unnecessary noise corrupting the temporal cues of intra-clip frames. In [44], the authors solve the problem applying the same transformation to all the frames of a mini-batch clip instead of randomly changing it for each frame. Random cropping, flipping and erasing are used to augment a video dataset for person re-identification. Image mixing techniques (e.g., Mixup [60] and CutMix [61]) have been widely used for image data augmentation. These types of approaches generate the augmented images mixing the pixel values from two different images of the original dataset. Some algorithms, (i.e., Mixup), averages the RGB values of the two images, while methods like CutMix replace randomly shaped patches of one image with the other. In order to extend image mixing techniques to video data augmentation, temporal cues in between frames must be taken into account. VideoMix [46] is a data augmentation method proposed by [PERSON] et al. that extends CutMix to video data augmentation. The temporal consistency is preserved keeping the patch size and position the same for all the frames of each video clip. The authors tested VideoMix on three tasks (action recognition, localization and detection) training different 3D CNNs. They compared the performances of their algorithm against the vanilla CutMix method. After training the SlowFast-50 network on the Mini-Kinetics dataset, VideoMix achieved the best improvement in accuracy (+2.4%) for action recognition. A different approach to generate synthetic video is warping some key frames with the use of optical flow fields. This technique is proposed by [28] to augment a dataset for salient regions detection in videos. Starting from video frames and their saliency masks, the authors generate a synthetic optical flow field. They then use these optical flow fields to warp the original frames in order to generate new synthetic data. The proposed data augmentation method was tested on FBMS and DAVIS datasets. To show the effectiveness of data augmentation, three versions of training sets were created: only synthetic images, mixed real and synthetic images, and only real images. After training on the three training sets, the saliency maps mean absolute error (MAE) was calculated for each version. Using only synthetic data resulted in a small increase in MAE compared to the mixed training set (\(7.65\to 9.27\) on FBMS, \(6.36\to 7.53\) on DAVIS), while the real images training set suffered from severe overfitting. [PERSON] et al. [37; 39] achieved data augmentation through changes in illumination of video datasets for background subtraction.