Multimodal Real-Time Anomaly Detection and Industrial Applications

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, Wav2Vec2, 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 realtime performance on standard hardware while maintaining high accuracy.