QBurst Vision Platform: Multi-Camera AI Visual Intelligence

The client is a prominent academic medical center headquartered in the United States. The healthcare system manages high-density hospital facilities requiring strict adherence to medical safety protocols, environment hygiene, operational SLAs, and patient privacy standards.

• Manual Compliance Auditing and Security Monitoring: Safety teams could not manually monitor multi-camera networks to spot blocked pathways, unauthorized entry, SLA violations, or PPE non-compliance in real time.
• Cross-Camera Context Tracking: Standard video analytics failed to maintain object identity across overlapping feeds, leading to disjointed tracking across premises.
• Privacy and Cost Pressures: Streaming continuous raw video feeds to the cloud created massive bandwidth costs and violated healthcare PII/data residency guidelines.
• Rigid Rule Configurations: Adding domain-specific detection rules required extensive custom code updates across existing surveillance software.

We deployed a hybrid AI visual platform using NVIDIA edge hardware for local inference and AWS for centralized intelligence and enterprise application integration.

• Edge Inference: Installed NVIDIA Jetson and Orin edge devices to process video feeds locally with YOLO and Mask R-CNN, enabling sub-second inference even in bandwidth-constrained settings.
• Real-Time Multi-Object Tracking: Integrated DeepSORT and custom probability layers to fuse data across overlapping feeds, keeping spatial continuity and object identity across camera views and facilities.
• Campus Security and Compliance Monitoring: Built dynamic rule engines and LLM decisioning layers to trigger alerts for unauthorized entry, blocked exits, PPE violations, or SLA breaches without code overhead.
• Automated PII Masking & Hybrid Routing.
• Unified Intelligence Dashboard.

• NVIDIA Edge Inference Deployment: Powered local camera nodes with NVIDIA Jetson/Orin devices running fine-tuned YOLO and Mask R-CNN models for low-latency visual analytics, with sub-second inference in bandwidth-constrained settings.
• Real-Time Multi-Object Tracking Pipeline: Combined YOLO detection with DeepSORT tracking and custom probability adjustment layers to correct tracking errors from missed frames and keep multi-camera object continuity.
• Campus Security Rule Engine: Configurable triggers for unauthorized entry, blocked exits, PPE violations, and SLA breaches, added as rules instead of custom code changes to the surveillance software.
• Privacy-First Edge/Cloud Split: Engineered on-premise PII masking and local edge rule validation to satisfy strict healthcare compliance guidelines before pushing data to AWS.
• LLM-Driven Dynamic Alerting: Embedded Large Language Models in the decisioning layer to evaluate complex scene context and dynamically generate incident alerts.
• Hybrid Deployment Stack: NVIDIA Triton as the inference server, Jetson Nano/Orin(depending on the hospital area size) as the GPU edge hardware, and AWS Greengrass for remote device management where needed.
• Open-Source Client-Owned Stack: Built the platform on open-source vision frameworks, delivering an enterprise-ready architecture with full client IP ownership.

• 95%+ Single-Camera Accuracy: Delivered high-precision detection for localized environment violations and instrument tracking.
• 90%+ Multi-Camera Accuracy: Maintained object identification and continuous tracking across complex multi-camera hospital facilities.
• 24/7 Autonomous Surveillance: Replaced manual auditing with continuous compliance and security monitoring, accelerating issue resolution times.
• Optimized Cloud & Bandwidth Expenses: Reduced cloud processing overhead through edge-level inference and filtering.