The MCU RAM size requirement for Simulink Multi-Object Tracker module
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Elad Kivelevitch 2023 年 7 月 24 日
It is possible to deploy trackers with less than 200K RAM, however the general answer to your question depends on how many detections you're expecting and how many objects you want to track.
I recommend that you look into the following examples to see how to limit generated code size.
The example shows how to use trackerJPDA but you can also use trackerGNN in the same way.
Prashant Arora 2023 年 7 月 24 日
編集済み: Prashant Arora 2023 年 7 月 24 日
The memory required by multi-object trackers depends on how you configure them. The trackers define properties like MaxNumTracks, MaxNumSensors, which directly impacts the memory footprint. In addition to that, I did some work in improving memory footprint of some of the multi-object trackers in Sensor Fusion and Tracking Toolbox in R2022a.
Generate Code with Strict Single-Precision and Non-Dynamic Memory Allocation - MATLAB & Simulink (mathworks.com)
The GNN (Multi-sensor, multi-object tracker using GNN assignment - Simulink (mathworks.com)) and JPDA(Joint probabilistic data association tracker - Simulink (mathworks.com)) tracker blocks can now generate code without requiring dynamic memory allocation and with strict single-precision. In addition, they expose certain parameters to further reduce the memory footprint. For example, there are memory management properties added to the block (check out parameters enabled with "Enable memory management"). To understand their meaning, consider reading embedded code generation section of the example - Processor-in-the-Loop Verification of JPDA Tracker for Automotive Applications - MATLAB & Simulink (mathworks.com)
You can also use static code metrics - Generate Static Code Metrics Report for Simulink Model - MATLAB & Simulink (mathworks.com) to analyze the memory footprint of the generated code. The metrics will provide you the memory required from the device for the chosen tracker configuration.
From experience, I think a GNN tracker with the following parameters:
Maximum 32 tracks
Maximum 32 measurements
Maximum cluster size of 5 tracks and 5 measurements
should consume < 200 KB RAM when dynamic allocation is disabled. I would encourage you to generate code and verify it with static code metrics to ensure.
Hope this helps. Please feel free to reach out here if you have additional questions regarding this.