Multimodal Data Fusion Techniques for Intelligent Surveillance and Automation

Abstract

Multimodal data fusion has emerged as a foundational methodology for enhancing the reliability, performance, and autonomy of intelligent surveillance systems. By integrating diverse data streams—such as visual, thermal, audio, and radar signals—modern automated surveillance frameworks can make robust decisions under dynamic and uncertain environments. This paper presents a structured overview of state-of-the-art multimodal fusion techniques, including early-, mid-, and late-fusion strategies and neural attention-based fusion architectures. The study further examines the role of multimodal integration in improving threat detection accuracy, situational awareness, and autonomous decision-making. Two hypothetical datasets are visualized to represent the impact of fusion on overall performance. The findings indicate that optimized multimodal fusion significantly boosts system precision and adaptability, offering promising advancements for next-generation smart surveillance and automation platforms.