Progress and outlook of visual tracking: Bibliographic analysis and perspective

Abstract

Benefitting from continuous progress in computer architecture and computer vision algorithms, the visual tracking field has earned its rapid development in recent years. This paper surveys this interesting field through bibliographic analysis on the Web-of-Science literature from 1990 to 2019. Specifically, statistical analysis methods are used to obtain the most productive authors and countries/regions, the most cited papers, and so on. In order to realize an in-depth analysis, the co-authors, co-keywords and keyword-author co-occurrence networks are built to intuitively exhibit the evolution of research hotspots and the collaboration patterns among world-wide researchers. Brief introductions of the topics that occur frequently in co-keywords networks are provided as well. Furthermore, existing challenges and future research directions within the visual tracking field are discussed, revealing that tracking-by-detection and deep learning will continue receiving much attention. In addition, the parallel vision approach should be adopted for training and evaluating visual tracking models in a virtual-real interaction manner.