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Intelligent Video Analytics Frank Yeh – Sr. Architect

Intelligent Video Analytics Frank Yeh – Sr. Architect Public Safety/Smarter Cities GOCOE Methodology for Analytics Design and Deployment (MADD). Definition & Purpose. MADD is a structured methodology for the design and deployment of IVA Analytics

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Intelligent Video Analytics Frank Yeh – Sr. Architect

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  1. Intelligent Video Analytics Frank Yeh – Sr. Architect Public Safety/Smarter Cities GOCOEMethodology for Analytics Design and Deployment (MADD)

  2. Definition & Purpose • MADD is a structured methodology for the design and deployment of IVA Analytics • The process defined should be followed to ensure successful Analytics deployments: • Understand Customer requirements and set realistic expectations • Collect relevant, usable information to ensure that use cases are supportable and delivery is successful • Define and design realistic Use Cases • Leverage scarce top-level resources effectively • Specific Roles and Functions are defined • Roles define explicit skill sets • Functions require explicit skill sets • Mapping of Functions to Roles is based on these skill sets • Based on functional skillset, each Role can lead to a certification IBM Confidential

  3. Role Definition • Analytics Practitioner (Technician ?) • Competence with IVA Analytics Administration • Camera/View Configuration • View Calibration (RoU, Scene Settings, Color) • Alert Definition • Users, Profiles, Health logs, etc. • Competence with Video and Image tools and utilities • Basic VMS usage • Collection of Camera Images & Video Clips • Analytics Architect • Competence with IVA Analytic Capabilities & Limitations • Basic Alert design (common use cases, camera view & activity level constraints) • Basic Alert tuning (alert parameters, common problems, basic false alert reduction) • Competence with collection and interpretation of IVA Event/Alert Metadata • Metadata statistics, Alert adjudication, basic false alert analysis • Competence ingesting video files into IVA • Creation of demonstrations • Testing analytics on customer supplied video clips • Familiarity with IVA tools • IBMSSE (aka Tuning Tool), Basic Search, configuration file management tool • Analytics Expert • Competence with Analytic algorithms • Understand why algorithms are generating metadata • Competence in complex Alert design / tuning • Difficult environments, Compound alerts, new / unusual use cases, • ComfortablemodifyingAnalytics Profiles • Competence with IVA tools IBM Confidential

  4. Question • Who is responsible for system-level “analytics” issues • Corrupted config files • SSE resource utilization problems • HD video processing techniques • Codec / Direct Show issues • … • Are these just L2/3 support, or should we require some familiarity on one of the more advanced roles ? IBM Confidential

  5. The Basic Process Underline indicates role with primary responsibility • Customer Interviews (Customer, Architect, Expert) • Understand Customer’s Pain Points • Survey existing Customer assets • Set realistic expectations • Use Case Definition (Architect, Expert)(Should the Expert be co-primary, or supporting ?) • Collect representative images from available cameras • Survey Possible new Camera Positions • Propose Potential Use Cases based on available Camera Views • Propose Potential new camera positions (Isn’t this the same as 2 ?) • Field Trials (Practitioner, Architect, Expert) • Implement representative subset of proposed cameras covering all use cases • Iterate: Examine Results, Tune analytics as required, Reject use cases that fail to meet customer needs • Document tuning “recipe” to guide deployment across all cameras. • Proposal & Commitment (Architect, Customer, Expert) • Use field trial results to propose use cases and accuracy expectations across all cameras • Examine rejected use Cases and propose solutions where possible • new camera positions, use case modifications • Return to Field Trial as needed • SeekCustomer’s agreement on proposal • Delivery (Practitioner, Architect, Expert) • Deploy proposed use cases and tune as per recipe • Refine tuning as needed, some cameras may still need to be rejected. IBM Confidential

  6. Roles and Interfaces Customer Expert Architect Practitioner IBM Confidential

  7. Process Iteration Customer Interviews Use Case Definition Implement Evaluate Field Trials Tune Proposal Commitment Implement Evaluate Delivery Tune IBM Confidential

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