Online Video Anomaly Detection
Author:
Zhang Yuxing1ORCID,
Song Jinchen1,
Jiang Yuehan1,
Li Hongjun12
Affiliation:
1. School of Information Science and Technology, Nantong University, Nantong 226019, China
2. State Key Lab. for Novel Software Technology, Nanjing University, Nanjing 210023, China
Abstract
With the popularity of video surveillance technology, people are paying more and more attention to how to detect abnormal states or events in videos in time. Therefore, real-time, automatic and accurate detection of abnormal events has become the main goal of video-based surveillance systems. To achieve this goal, many researchers have conducted in-depth research on online video anomaly detection. This paper presents the background of the research in this field and briefly explains the research methods of offline video anomaly detection. Then, we sort out and classify the research methods of online video anomaly detection and expound on the basic ideas and characteristics of each method. In addition, we summarize the datasets commonly used in online video anomaly detection and compare and analyze the performance of the current mainstream algorithms according to the evaluation criteria of each dataset. Finally, we summarize the future trends in the field of online video anomaly detection.
Funder
National Natural Science Foundation of China
Nanjing University State Key Laboratory for Novel Software Technology
Nantong Science and Technology Program
Postgraduate Research and Practice Innovation Program of Jiangsu Province
College Students’ Innovation and Entrepreneurship Training Project
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Cited by
2 articles.
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