Video Segmentation : Techniques, Applications, and Challenges

One of the core issues in computer vision is video segmentation, which is the process of dividing video content into meaningful and discrete pieces. It makes a wide range of applications possible, including video compression, action identification, object tracking, and video summary. The accuracy and efficiency of video segmentation tasks have significantly increased due to recent developments in deep learning, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The two primary kinds of video segmentation techniques—pixel-level segmentation and object-level segmentation—are outlined in this work. We examine important techniques, assess their effectiveness, and talk about the difficulties that contemporary video segmentation systems face, including handling occlusions, real-time processing, and temporal consistency. Additionally, new developments in the industry are highlighted in the study, including the usage of transformers and self-supervised learning.