Correlation analysis of publication volume in abnormal behavior detection: A knowledge network perspective
- DOI
- 10.2991/978-94-6463-419-8_12How to use a DOI?
- Keywords
- Deep learning; Abnormal behavior detection; Video surveillance; Artificial intelligence; CiteSpace
- Abstract
His study employed CiteSpace software to analyze research outcomes related to abnormal behavior detection based on video surveillance. The primary focus was on the China National Knowledge Infrastructure (CNKI) and “Web of Science” (WoS) databases. The objective was to identify research trends and provide valuable references for advancing the exploration of this research direction. The following conclusions were drawn: (1) The published literature on abnormal behavior detection models and algorithms exhibits a consistent growth trend over time; (2) The results of authors, institutions, and countries (from the WoS and CNKI databases) that have conducted research in this field were statistically analyzed. (3) In the last two years, domestic journals key words focused on behavior recognition and classroom monitoring, while foreign journals prioritized feature extraction.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Shilong Wang AU - Jinghuan Zhu AU - Li Chao PY - 2024 DA - 2024/05/07 TI - Correlation analysis of publication volume in abnormal behavior detection: A knowledge network perspective BT - Proceedings of the 3rd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2024) PB - Atlantis Press SP - 91 EP - 98 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-419-8_12 DO - 10.2991/978-94-6463-419-8_12 ID - Wang2024 ER -