This week, Xinyu was unable to attend class due to family matters, which slightly impacted overall progress and put his individual tasks slightly behind schedule. Despite this, he continued working on the vision pipeline, with a focus on improving its stability and robustness under real deployment conditions on the Raspberry Pi. In particular, he implemented fallback mechanisms such as dynamically adjusting input image resolution to balance detection accuracy and frame rate, as well as adapting the update frequency of bounding boxes so that the system can maintain smoother tracking when full detection cannot be performed at every frame. He also explored how these adaptive strategies interact with tracking logic to maintain consistent user identification under constrained compute.
Due to the reduced availability this week, progress is slightly behind schedule. However, the work done on improving system robustness is valuable for later integration and testing. Next week, Xinyu plans to dedicate more time to the project, focusing on completing integration with the control pipeline and conducting end-to-end testing on hardware to recover the schedule.
Additional section: New tools, knowledge, and learning strategies
During this phase of the project, Xinyu needed to learn several new tools and concepts related to embedded vision deployment and system integration. This included understanding how to optimize deep learning inference on resource-constrained devices like the Raspberry Pi, working with camera drivers and system-level configurations, and tuning parameters for real-time performance such as resolution scaling and frame scheduling. He also gained experience with stereo vision processing, including generating and interpreting disparity maps for depth estimation.
To acquire this knowledge, Xinyu primarily used informal learning strategies. He referred to official documentation for libraries such as OpenCV and YOLO, read technical discussions on forums and GitHub issues to resolve compatibility and performance problems, and watched online tutorials to better understand deployment workflows. In addition, he relied heavily on iterative experimentation, testing different configurations and observing system behavior to develop a practical understanding of performance trade-offs and system limitations.