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China is a global leader in computer vision surveillance research. This is the conclusion of a recent report from the Center for Security and Emerging Technology (CSET) at Georgia University.
According to the report, titled Trends in AI Research for the Visual Surveillance of Populations, China’s research sector produces ‘a disproportionate share’ of research into three core AI-related surveillance technologies.
China is leading three key areas: person re-identification (REID, associating images of the same person taken from different cameras or from the same camera in different occasions), crowd counting, and spoofing detection (i.e. technologies that aim to expose attempts to subvert identification technologies).
Additionally, China’s research community publishes a notably higher percentage of papers on human-facing computer vision tasks which, the paper argues, represent supporting technologies for wider surveillance solutions that use machine learning. These tasks include emotion recognition, face recognition, and action recognition.
The authors concluded that within the visual surveillance sector, the study finds that face recognition was the most recurrent task, appearing in more than a thousand papers for the year 2019. However, the authors note that crowd-counting and face-spoofing recognition are ‘fast growing’ fields of pursuit.
The report argues: ‘These algorithms are often applied for benign, commercial uses, such as tagging individuals in social media photos. But progress in computer vision could also empower some governments to use surveillance technology for repressive purposes.’
China’s dominance in computer vision is clear: ‘Researchers with Chinese institutional affiliations were responsible for more than one third of publications in both computer vision and visual surveillance research. ‘This makes China by far the most prolific country in both areas. Chinese researchers’ share of global visual surveillance research is growing at a similar rate to their share of computer vision research.’
The authors of the paper observe that their study only touches on English language scientific papers, and that extending it to non-Anglophone publications could reveal a deeper iceberg of academic endeavor from China in these sectors. Analyzing public and openly published papers cannot account for private corporate or state research, and classified research, but is a workable index of sector activity in the absence of these hidden data points.
How was the research conducted? According to unite.ai, the report represents the application of Natural Language Processing (NLP) approaches to a dataset of published papers covering the years 2015-2019. The authors derived core data by training a SciREX document-level information extraction model on data from Papers With Code (a website that organizes access to technical papers that also provide the software used to create the paper’s findings). The model was then applied to an aggregated CSET body of scholarly literature containing more than 100 million individual publications across six academic datasets.