Bibliometric Analysis of Scientific Production of Intelligent Video Surveillance

This is the authors' abstract. We don't add key points for this paper.

This article offers a bibliometric analysis of academic research in intelligent video surveillance, evaluating its evolution between 2000 and 2024. 1,343 documents were collected from the Scopus database and the PRISMA methodology was applied to organize the search and selection of relevant publications. The findings show a notable increase in the number of studies, reaching its highest point in 2022, driven by advances in artificial intelligence, the Internet of Things (IoT) and deep learning. China leads scientific production in this field, followed by India and the United States. Main research areas include real-time surveillance using deep learning methods, sequential and transfer learning techniques, as well as the use of advanced YOLO, Faster-RCNN and RFCN algorithms in controlled environments; however, detecting unusual behavior is a latent challenge.