VisionGuard Command Center

Autonomous industrial safety coordinating OpenCV 5 accelerated via COOL on AWS Graviton3 with an Amazon Bedrock (Claude 3.5 Sonnet) agentic reasoning loop and human-in-the-loop auditability.

-58.4%

Net Cost Reduction

+61.5%

Throughput (FPS)

-38.0%

p95 Frame Latency

100%

Human Review Gate

01 // Interactive CCTV Perception & Triage Lab

REC CAM_WAREHOUSE_02

BAY 02 — OVERHEAD CRANE BUFFER

COOL_GRAVITON_ARM64

ZONE: ZONE_CRANE_RESTRICTED

KALMAN TRACK: 3.8s PERSISTENCE

LATENCY: 3.84 ms

Select Hazard Scenario

Agentic Trigger Threshold (Current: 0.75)

0.50 0.90 0.75

02 // AWS Step Functions State Machine Execution

01 / INGEST

S3 Video Event

EventBridge

02 / TRIAGE

OpenCV 5 + COOL

3.84 ms (ARM64)

03 / GATE

Confidence Check

< 0.75 Threshold

04 / REASONING

Bedrock (Claude 3.5)

Tool: inspect_frames

05 / VERIFY

Kalman & PPE

Spot 5 FPS Decimated

06 / HUMAN GATE

TaskToken Review

.waitForTaskToken

07 / RESOLUTION

DynamoDB & SNS

Audit Logged

03 // Structured Decision Traces & Bedrock Tool Calls

04 // Responsible AI: Human-in-the-Loop TaskToken Gate

STATUS: WAITING FOR SUPERVISOR SIGN-OFF

Incident #inc_8f3a9b | Crane Buffer Intrusion

AWS Step Functions execution paused on TaskToken. Visual evidence frame extracted and delivered for human validation before incident escalation.

          • Target Entity: ent_360_0 (Stationary in Zone)

          • Kalman Persistence: 3.8s confirmed

          • PPE Classifier: High-Vis Vest Present (0.96) | Hard Hat Absent (0.94)

AUDIT TRAIL LOG

DynamoDB State & Accountability Record

          [00:12.0] Zone intrusion detected (conf: 0.68, unverified)

          [00:13.1] Bedrock Claude 3.5 Sonnet called tool: inspect_more_frames

          [00:15.8] Temporal tracking verified: worker stationary in zone for 3.8s

          [00:15.9] PPE violation confirmed: hard hat absent (conf: 0.94)

          [00:16.0] Step Functions paused on TaskToken: AAAA-KgAA...

05 // Verified COOL Benchmark (10,000 Frames @ 1280×720)

Metric	Baseline (x86 On-Demand)	Candidate (COOL on Graviton3)	Delta / Efficiency
p95 Latency	42.15 ms	26.13 ms	-38.0% faster
Throughput	23.4 FPS	37.8 FPS	+61.5% FPS
Memory Footprint	412.5 MB	285.4 MB	-30.8% smaller footprint
Cost per 1,000 Frames	$0.000577	$0.000240	-58.4% net cost reduction

Interactive Production Cost Calculator

Adjust monthly video frame volume across multiple CCTV streams to calculate actual production savings:

1M frames 50M frames 10M frames/mo

x86 Baseline Cost

$5,770/mo

VisionGuard (COOL + Graviton3)

$2,400/mo

Net Annual Savings (-58.4%)

$40,440/yr

06 // AWS Platform Verification & Live Deployment Telemetry

AWS Graviton3 & Cloud-Native Verification

STATUS: READY FOR LIVE TELEMETRY

This container environment and pipeline execution is backed by committed ARM64 Graviton3 Docker definitions, AWS CDK v2 infrastructure stacks, and reproducible evaluation data.

AWS HARDWARE SPECIFICATION Instance Family: AWS Graviton3 (c7g.xlarge)
CPU Arch: aarch64 (Neoverse-V1, ARMv8.4-A)
Pricing: $0.03238 / vCPU-hr (Fargate ARM64)

ARM KLEIDICV & NEON ACCELERATION HAL Backend: Arm KleidiCV Vector Kernels
SIMD Acceleration: NEON + FP16 Enabled
Health Check: src/verify_cool.py (Status: PASS)

ORCHESTRATION & STATE MACHINE AWS Step Functions: VisionGuard-Agentic-Loop
Human Review: .waitForTaskToken SQS Pattern
Model: Claude 3.5 Sonnet on Amazon Bedrock

LIVE AWS CLOUD ENDPOINT (Optional / Test Suite) c7g.xlarge / ECS Fargate

When deployed via cdk deploy, events can be dispatched directly to the Step Functions state machine or API Gateway endpoint.


            curl -X POST https://visionguard.kbytehub.cloud/api/triage -d '{"zone_id": "ZONE_CRANE_RESTRICTED", "conf": 0.68}'