Video Object Segmentation for Object Detection and Recognition

Abstract

The crucial role of Video Object Segmentation is evident in various applications such as medical image diagnosis, industrial inspection, satellite image processing, autonomous driving cars, and human body parsing. This process involves segmenting an image into multiple instances or segments by annotating each pixel in the figure, which is considered a pixel-level classification problem that demands higher accuracy than image-level classification or object-level detection. One of the significant challenges in video object segmentation is the complexity of scenes in our environment, making object detection and recognition difficult. To address this challenge, convolutional networks are used, as there may be hidden layers in the input. Despite being a long-lasting challenge in the computer science field, various algorithms have been accepted to solve and improve video object segmentation problems. Convolutional Neural Networks (CNNs) have become an essential tool in the field of computer vision, as they have increased the performance of problems such as image classification and object detection.