This week I mainly focused on preparing for the interim demo, especially on the vision side. I recorded videos to demonstrate the current vision pipeline and its performance, and worked on designing a color frequency analysis method to help distinguish the target user across different bounding boxes. This is intended to improve person identification in multi-person scenarios by adding an additional signal beyond detection and tracking. I also trained our vision model using both our homegrown dataset and the COCO dataset, and evaluated it on a COCO test split to obtain baseline accuracy results for person detection.
In addition, we attempted to test the robot’s motion behavior, including speed and responsiveness, but encountered issues with the battery running out during testing. This limited the amount of hardware validation we could complete this week. As a result, my progress is slightly behind schedule. However, we believe this delay is manageable and plan to make up for it by continuing testing and integration early next week once the hardware is fully operational again.
Additional section: For verification of my vision and perception subsystem, I have started evaluating both detection accuracy and runtime performance. I trained the model on our homegrown dataset and the COCO dataset, and evaluated it on a COCO test set to obtain baseline accuracy metrics such as detection confidence and qualitative correctness. In addition, I plan to measure frame rate (FPS) on the Raspberry Pi under different input resolutions to verify that the system meets the real-time requirement. For the color-based person identification component, I will compare consistency of predicted identities across frames in multi-person scenarios. The results will be analyzed by checking whether detection accuracy remains high and whether the system maintains stable tracking at or near the required frame rate. These tests directly verify that the perception pipeline satisfies both accuracy and real-time constraints.