Multi-Camera, Multi-Viewpoint Inferencing With DSL Configuration
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Solution Overview
Problem
Current video analysis solutions lack the comprehensive and flexible features necessary to address sophisticated business challenges, such as monitoring machine and worker productivity, safety compliance, and yield calculations, often requiring costly custom development projects.
Innovation Solution
A configurable, domain-specific language (DSL) for video analysis that allows users to tailor systems to specific operational needs, enabling precise specification of video analysis tasks, with a modular architecture and dynamic allocation of inferencing tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a configurable DSL system is implemented, then adaptability and versatility are improved, but device complexity increases
Solution Approach 1:
A Domain-Specific Language (DSL) is introduced as an intermediary layer between the user and the complex video analysis system. The DSL provides a simplified, high-level configuration interface that translates user-friendly specifications into detailed system parameters, thereby improving adaptability without exposing the underlying complexity of the video analysis engine.
Solution Approach 2:
The video analysis system is segmented into modular components including the DSL parser, video stream processors, object detection modules, and result aggregation services. This modular architecture allows each component to be independently configured and maintained, reducing overall system complexity while enhancing adaptability through selective customization.
2Measurement precision
If custom development projects are undertaken for advanced video analysis, then measurement precision and analysis capability are improved, but loss of time and productivity are worsened
Solution Approach 1:
The system implements a universal video analysis platform capable of handling multiple advanced use cases including worker safety monitoring, machine productivity analysis, inventory tracking, and yield calculations. By providing a single multi-functional system with DSL-based configuration, the need for separate custom development projects for each application is eliminated, reducing implementation time while maintaining high analysis capability.
Solution Approach 2:
The system includes pre-configured video analysis pipelines, object detection models, and processing templates that can be directly applied to common use cases. These preliminary configurations allow users to deploy advanced video analysis capabilities immediately through DSL specification without requiring time-consuming custom development, while still achieving high measurement precision.
3Ease of operation
If generic video monitoring tools are used, then ease of operation is improved, but measurement precision and analysis capability are worsened
Solution Approach 1:
The system dynamically adapts its analysis capability based on the DSL configuration. Users can specify the level of detail and type of analysis required for each use case, allowing the system to optimize its processing depth. This dynamic configuration enables the system to provide simple monitoring when needed while delivering sophisticated analysis when required, maintaining ease of operation across different complexity levels.
Solution Approach 2:
The DSL enables different levels of analysis precision to be applied to different regions or aspects of the video stream. For example, high-precision object detection can be applied to specific zones of interest while other areas receive standard monitoring, optimizing both ease of operation and measurement precision according to local requirements.
Data Source
AI summary
This invention introduces a versatile system and methodology for configurable video inferencing analysis, utilizing a domain-specific language (DSL) to define detailed specifications for video analysis tasks across various domains, including but not limited to security surveillance, traffic monitoring, industrial automation, retail behavior analysis, and environmental observation. The modular architecture of the system encompasses an orchestrator module, multiple inferencing modules (watchers), a data management module, and a publication module, each tailored to execute configurations delineated in the DSL. This DSL supports a wide array of formats such as YAML, JSON, and XML, catering to diverse user preferences and integration requirements.