Evaluating Deep Learning Techniques for Real-Time Video Stabilization in 2D Scenes

In computer vision, removing unintentional camera motion from films with the goal of boosting their visual quality and enriching the viewing experience is known as video stabilization. Deep learning-based methods have drawn a lot of attention lately because of their capacity to recognize intricate motion patterns and successfully stabilize video sequence. The article described the Convolutional neural networks (CNNs), Generative adversarial networks (GANs) and recurrent neural networks (RNNs) are few of the deep learning architectures that are being investigated for video stabilization. This work includes a comparative analysis 2D scenes of many typical deep learningbased video stabilization algorithms, examining their methodologies, stabilization networks, and performance metrics in order to provide a thorough review. The merits and demerits of each strategy are shown by the quantitative and qualitative outcomes from various datasets.