Multi-camera spatiotemporal deep learning framework for real-time abnormal behavior detection in dense urban...

Area of Science:

• Computer Vision
• Artificial Intelligence
• Surveillance Systems

Background:

• Urban environments face challenges in real-time abnormality detection due to increasing density.
• Existing methods struggle with occlusion, dynamic scenes, and computational inefficiency, leading to false positives and poor generalization.
• Traditional and current deep learning models fail to capture complex social interactions and spatiotemporal dependencies in crowded scenarios.

Purpose of the Study:

• To propose a novel deep learning framework for multi-camera abnormal behavior detection using spatiotemporal information.
• To address the limitations of existing methods in handling complex interactions, computational load, and generalization to unseen anomalies.
• To enhance real-time crowd surveillance capabilities with adaptive scalability and resource provisioning.

Main Methods:

• Multi Scale Graph Attention Networks (MS-GAT) for interaction-aware anomaly detection.
• Reinforcement Learning Based Dynamic Camera Attention Transformer (RL-DCAT) for optimizing surveillance focus and reducing computational overhead.
• Spatiotemporal Inverse Contrastive Learning (STICL) with an anomaly memory for improved generalization to rare anomalies.
• Neuromorphic event-based encoding using spiking neural networks for fast action analysis.
• Generative Behavior Synthesis and Meta-learned Few-Shot Adaptation (BGS-MFA) for synthesizing new abnormal behaviors and few-shot adaptation.

Main Results:

• MS-GAT reduced false positives by up to 30%.
• RL-DCAT reduced computational overhead by 40% and increased recall by 15%.
• STICL improved recall for unseen anomalies by 25%.
• Neuromorphic encoding lowered detection latency by 60%.
• BGS-MFA improved anomaly detection generalization by 35%.
• Overall framework evaluation showed a 40% reduction in false alarms, 50% lower computational demands, and 98% real-time efficiency on benchmark datasets.

Conclusions:

• The proposed multi-faceted deep learning framework effectively addresses the limitations of current abnormal behavior detection systems.
• The integration of MS-GAT, RL-DCAT, STICL, neuromorphic encoding, and BGS-MFA provides a robust solution for real-time multi-camera crowd surveillance.
• The framework demonstrates significant improvements in accuracy, efficiency, and generalization, paving the way for advanced real-world applications.