Multimodal Real-Time Anomaly Detection and Industrial Applications

Abstract--This paper presents the design, implementation, and evolution of a comprehensive multimodal room monitoring system that integrates synchronized video and audio processing for real-time activity recognition and anomaly detection. We describe two iterations of the system: an initial lightweight implementation using YOLOv8, ByteTrack, and Audio Spectrogram Transformer (AST), and an advanced version incorporating multi-model audio ensembles, hybrid object detection, bidirectional cross-modal attention, and multi-method anomaly detection. The evolution demonstrates significant improvements in accuracy, robustness, and industrial applicability. The advanced system combines three audio models (AST, Wav2V ec2, HuBERT) for comprehensive audio understanding, dual object detectors (YOLO and DETR) for improved accuracy, and sophisticated fusion mechanisms for enhanced cross-modal learning. Experimental evaluation shows the system's effectiveness in both general monitoring scenarios and specialized industrial safety applications, achieving real-time performance on standard hardware while maintaining high accuracy. The increasing demand for intelligent monitoring systems in smart homes, security applications, healthcare facilities, and industrial environments has driven significant research into multimodal perception systems. Traditional single-modality approaches, whether relying solely on visual or audio information, often fail to capture the complete context of activities and events. Visual systems may miss audio cues that provide crucial context, while audio-only systems lack spatial understanding. This paper presents a comprehensive journey through the development of a multimodal room monitoring system, documenting both an initial lightweight implementation and its evolution into an advanced industrial-grade solution.