Abnormal Human Behavior Detection in Videos: A Review


  • Huiyu Mu China Agricultural University
  • Ruizhi Sun China Agricultural University https://orcid.org/0000-0001-7267-5283
  • Gang Yuan China Agricultural University
  • Yun Wang China Agricultural University




abnormal detection, video surveillance, behavior representation, event modeling


Modeling human behavior patterns for detecting the abnormal event has become an important domain in recent
years. A lot of efforts have been made for building smart video surveillance systems with the purpose of
scene analysis and making correct semantic inference from the video moving target. Current approaches have
transferred from rule-based to statistical-based methods with the need of efficient recognition of high-level
activities. This paper presented not only an update expanding previous related researches, but also a study covered
the behavior representation and the event modeling. Especially, we provided a new perspective for event
modeling which divided the methods into the following subcategories: modeling normal event, prediction
model, query model and deep hybrid model. Finally, we exhibited the available datasets and popular evaluation
schemes used for abnormal behavior detection in intelligent video surveillance. More researches will promote
the development of abnormal human behavior detection, e.g. deep generative network, weakly-supervised. It is
obviously encouraged and dictated by applications of supervising and monitoring in private and public space.
The main purpose of this paper is to widely recognize recent available methods and represent the literature in
a way of that brings key challenges into notice.

Author Biographies

Ruizhi Sun, China Agricultural University

Ruizhi Sun received the M.S and Ph.D degrees in Tsinghua University, China. He is currently with the department of College of Information & Electrical Engineering at China Agricultural University, China. Prof. Sun served on numerous Technical Program Committees for CCF conferences. He is a Senior Member of CCF. His areas of interest include computer vision, artifcial intelligence and computer networks.

Gang Yuan, China Agricultural University

Gang Yuan graduated from China Agricultural University-China with Doctoral degree. She is currently with the department of College of Information & Electrical Engineering at China Agricultural University, China. Her areas of interest include artifcial intelligence.

Yun Wang, China Agricultural University

Yun Wang is a PhD student in college of Informatio n and Electrical Engineering, China Agricultural University, China. She received the MS degree in Henan Normal University, Henan, China. Her current research interests include computer vision, pattern recognition.