Examination of Object Tracking Studies using Deep Learning: A Bibliometric Analysis Study

Abstract

The increasing amount of data obtained with advancing technology has led to the growing popularity of deep learning algorithms. Within the scope of this study, the aim was to examine the use of object-tracking algorithms, which are one of the most commonly used areas of deep learning. To contribute to the literature, a bibliometric analysis was conducted. For this purpose, a bibliometric analysis was carried out on 1209 articles accessed through the Web of Science database using the keywords 'deep learning' and 'object tracking'. Voswiever (version 1.6.20) and R studio Bibliometrix package programs were used for this purpose. In the analysis, it was determined that the highest number of publications was reached in 2022 among the studies conducted between 2012 and 2024. Additionally, it was observed that the majority of the studies were conducted in China, the USA, and South Korea. The prominent keywords in these articles were deep learning, object tracking, object detection, target tracking, and feature extraction, in that order. The research indicates a growing trend in the use of deep learning in the field of object tracking in recent years. Furthermore, it was identified that object detection, which stands out in object tracking studies, is also a popular research topic. It is believed that this study will pave the way for further research in this area. © 2024 IEEE.