<jupyter_start><jupyter_text>3. High Level System Design[index](../Index.ipynb) | [prev](./02.LiteratureReview.ipynb) | [next](./04.DataCollection.ipynb) The system design, proposed as part of this research, can be described as a two high level components:- Real time frame processing used for object detection, and anomaly detection- Batch processing used for forecasting, and other kind of anomaly detection**Note:** This design is a proof of concept. Only individual components are developed as part of this research.  3.1. Real time frame processingFig. 3.1. System Design - Real Time Processing<jupyter_code>Image("../Resources/img/system-diagram-1.png")<jupyter_output><empty_output><jupyter_text>**Diagram description:**- Raspberry Pi camera (1) generates frames- Frames, as *jpeg buffers*, flow to a Desktop PC via high performance message queue - ZMQ (2)- Processing is applied to each frame:    - resize, apply motion sensing, detect object classes and their $x,y$ coordinates    - save images on SSD Drive (4), for further analysis and batch processing    - emit all frames to connected end users (5)- Client web browser application is served by NodeJS web server to render a UI (3)The data flow is described in more detail in the [next chapter - Data Collection](./04.DataCollection.ipynb).  3.2 Batch processingFig. 3.2. System Design - Batch Processing<jupyter_code>Image("../Resources/img/system-diagram-2.png")<jupyter_output><empty_output>
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