This week I focused on getting the YOLO object detection model running on our Raspberry Pi so that we can start testing the vision component of SafeFollow. I worked on setting up the runtime environment, installing the necessary dependencies, and running initial inference with the camera input. One challenge I encountered was the frame processing rate. Because the Raspberry Pi does not have a dedicated AI accelerator, running YOLO on the CPU can result in very low frame rates, sometimes only a few frames per second, which makes real-time tracking difficult.
To address this, I experimented with reducing the input image resolution and adjusting the camera capture settings to balance accuracy and speed. I also explored using lighter YOLO model variants and optimizing the inference pipeline. These adjustments helped improve the processing speed somewhat, although performance tuning is still ongoing. Next week, I plan to continue optimizing the pipeline and integrate the detection output with our person tracking module.