Bibliometric Analysis of Publications on Action Recognition; Convolutional Neural Network; Video Surveillance During 2011-2021

Action recognition based on convolutional neural networks (AR-CNN) has been developing rapidly in recent years. It is of great significance to conduct a deep analysis to understand the recent development of AR-CNN. However, a limited study examining the research status of this field could be found. Therefore, this study aims to quantitatively assess the publications related to the SciVal topic "Action Recognition; Convolutional Neural Network; Video Surveillance (T.561)" in computer vision research. This study focused on six aspects: literature distribution characteristics analysis, the development trend, citation analysis, collaborative analysis, keyword analysis and thematic evolution, using VOSviewer and Bibliometrix. The relevant publications were retrieved from Scopus in the period 2012–2021. A total of 6633 publications were identified by 9088 different authors, 62% were conference papers, and 35% were research articles. China and the USA contributed 39.7 % and 17.9 % of the total publications, respectively. The authors' production according to Price's Law was inconsistent but consistent according to Lotka's Law. Ling Shao was the most productive author, with 48 papers (%0.7). Chinese Academy of Sciences was the most productive affiliation, with 259 papers (%3.9). The first Bradford site consisted of Computer Science Lecture Notes with 617 publications. A moderately significant correlation was revealed between the country's publications and GDP per capita. The overall results show that the number of AR-CNN-related documents has increased significantly in recent years, with rapid growth from 2017. Although publications on AR-CNN were published mainly in European journals, China led the scientific production.