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A Survey on Deep Learning Technique for Video Segmentation.

Tianfei Zhou, Fatih Porikli, David J Crandall

IEEE Transactions on Pattern Analysis and Machine Intelligence

|November 30, 2022

Summary

This summary is machine-generated.

This survey reviews deep learning for video segmentation, covering generic object and semantic segmentation. It details methods, datasets, and challenges, offering insights into future research directions for this computer vision task.

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Area of Science:

Computer Vision
Deep Learning
Artificial Intelligence

Background:

Video segmentation is crucial for applications like autonomous driving and virtual backgrounds.
Deep learning approaches have recently shown significant advancements in video segmentation performance.

Purpose of the Study:

To comprehensively review two primary research areas: generic object segmentation and video semantic segmentation.
To provide an overview of task settings, historical development, and challenges in video segmentation.
To benchmark existing methods and identify future research opportunities.

Main Methods:

Review of deep learning-based methods for generic object segmentation and video semantic segmentation.
Detailed analysis of representative literature, including datasets and methodologies.
Empirical benchmarking of reviewed methods on established datasets.

Main Results:

A comprehensive overview of the state-of-the-art in video segmentation.
Performance benchmarks highlighting the strengths and weaknesses of different approaches.
Identification of current limitations and open challenges in the field.

Conclusions:

Deep learning has significantly advanced video segmentation capabilities.
Further research is needed to address open issues and explore new opportunities.
A public resource is available for tracking ongoing developments in video segmentation research.