Fuzzy Logic in Surveillance Big Video Data Analysis: Comprehensive Review, Challenges, and Research Directions

CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term Big Video Data (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognitionmethods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real-world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this article, we draw researchers' attention toward the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook toward future research directions derived from our critical assessment of the efforts invested so far in this exciting field.

Automated summary

Continuous surveillance by CCTV cameras generates large amounts of data known as Big Video Data (BVD). However, the usage of this data is hindered by the limited capabilities, high computational complexity, and strict installation requirements of existing methods. This article focuses on the usage of fuzzy logic as a complementary approach to overcome these challenges in surveillance applications based on BVD. A comprehensive literature survey is conducted to study different methods for video analysis that incorporate fuzzy logic concepts. The advantages, downsides, and challenges of these methods are discussed, along with an outlook on future research directions.