ISSN 1000-1239 CN 11-1777/TP

• 综述 •

视频拷贝检测方法综述

1. (复旦大学计算机科学技术学院 上海 201203) (gujw15@fudan.edu.cn)
• 出版日期: 2017-06-01
• 基金资助:
国家自然科学基金优秀青年科学基金项目(61622204)

Video Copy Detection Method: A Review

Gu Jiawei, Zhao Ruiwei, Jiang Yugang

1. (School of Computer Science, Fudan University, Shanghai 201203)
• Online: 2017-06-01

Abstract: Currently, there exist large amount of copy videos on the Internet. To identify these videos, researchers have been working on the study of video copy detection methods for a long time. In recent years, a few new video copy detection algorithms have been proposed with the introduction of deep learning. In this article, we provide a review on the existing representative video copy detection methods. We introduce the general framework of video copy detection system as well as the various implementation choices of its components, including feature extraction, indexing, feature matching and time alignment. The discussed approaches include the latest deep learning based methods, mainly the application of deep convolutional neural networks and siamese convolutional neural networks in video copy detection system. Furthermore, we summarize the evaluation criteria used in video copy detection and discuss the performance of some representative methods on five popular datasets. In the end, we envision future directions on this topic.