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Demo listing. This page uses illustrative seed metadata to demonstrate the Atlas. Claims shown here are not verified product facts.

HerdVista

Battery-first edge vision service that runs deep multiple-object tracking on Jetson-class nodes from the live camera frame only — no cloud…

A battery-first edge vision service that runs deep multiple-object tracking on Jetson-class nodes from the live camera frame only — no cloud round-trip — for outdoor and mobile IoT perception.

DemoDeployment-Ready FoundationfazeZERO VerifiedProprietaryInternet of Things & Edge

Last verified 15 Sept 2026 · Updated 23 Sept 2026 · Metadata v4

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Configuration + implementation engagement

Problem

Cities, campuses, and industrial sites that need real-time multi-object tracks on mobile/outdoor poles where coverage is poor and power is budgeted in watts, not rack PDUs.

Outcome

A deployment-ready foundation that teams can configure into production.

Who uses it

  • Operations Analyst
  • Solution Architect

Key capabilities

  • Core workflow

    AI-assistedIncluded

    Primary operating workflow for this foundation.

  • Document intelligence

    AI-assistedIncluded

    Extract and ground decisions from operating documents.

Typical workflow

Primary operating flow

Trigger: An operator starts the primary use case

  1. Task

    Stage 1: Capture inputs

    Operator

  2. AI-assisted

    Stage 2: Assist with draft

    System

  3. Human checkpoint

    Stage 3: Human approval

    Approver

Task Automated AI-assisted Human checkpoint Decision

About this foundation

Live-frame multi-object tracking for IoT edge nodes. Cloud CV fails the paper’s real-time definition (only the current live frame, no delayed queue). HerdVista productizes Jetson TX2-class deployment (about 5–15 W, Max-Q ~7.5 W efficiency point vs Max-N up to 15 W), onboard camera + wireless, and the dETRUSC-style evaluation discipline for power and frame rate — foreground segmentation plus deep tracking as an operable edge SKU.

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