On the use of semantic technologies for video analytics

Abstract

The rapid proliferation of smart devices, surveillance cameras, infrastructures and buildings enhanced with the Internet of Things (IoT) technologies has led to a huge explosion of contents, especially in the video domain, determining an ever increasing interest towards the development of methods and tools for automatic analysis and interpretation of video sequences. Through the years, the availability of contextual knowledge has proven to improve video analysis performances in several ways, although the formal representation of semantic content in a shareable and fusion oriented manner is still an open problem, also considering the wide diffusion of Fog and Edge computing architectures for video analytics lately. In this context, an interesting answer has come from Semantic Web (SW) technologies, that opened a new perspective for the so-called Knowledge Based Computer Vision (KBCV), adding novel analytics opportunities, improving accuracy, and facilitating data exchange between video analysis systems in an open extensible manner. In this work, we propose a survey of the papers from the last eighteen years, back when first applications of semantic technologies to video analytics have appeared. The papers, analyzed under different perspectives to give a comprehensive overview of the technologies involved, reveal an interesting trend towards the adoption of SW technologies for video analytics scopes. As a result of our work, some insights about future challenges are also provided.