This service is intended principally for providing a professional team of individuals who are responsible for deploying, configuring, tuning, and supporting Multi-vendor Intelligent Video Analytics. These services includes solution architects, delivery teams, and level 1, 2 and 3 support personnel.
While Multi-vendor IVA Knowledge Centers describes in depth the mechanics of creating and calibrating video analytic engines. The IVA administrator must make several decisions in this process. These decisions are based on a number of factors, including knowledge of the underlying use-case, the analytic capabilities and limitations, the camera viewpoints, and the accuracy requirements. Sometimes it is necessary to delve deeper into the system configuration and “tune” components to get the best performance to meet your case needs.
Our analytic planning and configuration Services help with this process. Our teams have the experience of installing and configuring IBM IVA. This includes understanding of analytic profiles, analytic engines, alerts, and calibration. Our knowledge of computer vision is coupled with technical concepts are renders a a high level of expertise.
1.2. Overview These services focuses on details of the design and implementation of deploying analytic use-cases for IBM Intelligent Video Analytic (IBM IVA). Analytic Deployment Model discusses the process of deploying analytic solutions, how this document fits into that model, and companion documents that cover other aspects of analytic deployment. System Overview defines basic terminology, describes major components, and positions the SSE in the context of the overall IBM IVA system.
Scene Context Framework describes the component of IBM IVA that is responsible for managing all “context” data in the system. This is integrated with a Camera Position Monitoring facility that detects camera viewpoint changes, and associates context data with a detected position.
Video Source Requirements and Video Quality Requirements explain how, in order to get the best results, some minimal requirements on video source frame rate and camera resolution must be achieved. In addition, key video quality factors are determined. These factors might require tuning to further improve analytic results.
Analytic Profiles provides an introduction to profiles that function as an engine “template” upon which new engines are created. This is followed by a comparison of different analytic profiles:
Object Analysis Analytics focuses on tracking two types of moving objects, people and vehicles, in general urban surveillance contexts.