Month: March 2025

This week, I worked on debugging the camera with Eric and verifying the functionality of everything else. We tried many troubleshooting methods on both our camera module 3 and the backup arducam camera. However, no matter what we did we could not get the camera module 3 to detect and take pictures. The backup camera does detect, but would only take extremely blurry pictures of weird colors, such as blue and pink despite facing a white wall. Ultimately, we decided to move forward with a usb webcam Tzen-Chuen already had for demos. Outside the camera, mostly everything else was tested to see if it ran smoothly. The whole pipeline from image capture (simulated with just a downloaded image file on the raspberry pi) to being verified as a match was tested and worked within timing requirements. In addition, I fixed some bugs in our website such as images not displaying in fullscreen properly. The link is the same as the previous week. 

Outside of the camera issues, my progress has been on schedule. Since we don’t think putting the camera into our pipeline is much of an issue once we have a working one, we don’t expect this to delay much. By next week, I hope to add a working camera to our pipeline and then work on the sagemaker implementation with Eric.

This week I focused on finalizing the locally run code. I added the queue functionality where if a match cannot be sent up to the database for whatever reason, such as loss of internet connection, the data will be saved locally, and it will be retried later. I have also written a main function that will take a picture and send matches every 40 seconds, check for database updates every 200 seconds, and retry failed entries to the database every 200 seconds. Due to some problems we have with initializing the camera, that is the only aspect of the locally run code we do not have working right now. The code can be found here. In addition to the locally run code, I have made a prototype for the front-end website using lovable for law enforcement to look for matches. They can see the currently active amber alerts, possible matches sent to the database, and verified matches where the larger models on the cloud confirm the result of the possible matches. It also has useful filtering options such as filtering by confidence level or license plate number. The website can be found here.

My progress is on schedule. By next week, I hope to implement the verification process of possible matches into verified matches with Eric, and have an MVP.

This week I focused on the integration between the Raspberry Pi and the cloud.  I worked on creating the database update code, where a Python function will retrieve the latest version of the Amber Alert database from the supabase and update the locally saved database with it. If the Raspberry Pi does not have an internet connection, it will simply continue using the locally saved version.  I also wrote the code to check for matches in the database. A loop checks the OCRed text with the database and if a match is found, the original image is uploaded to a supabase bucket, and an entry is made into a “possible matches” table for further verification by the cloud models. We will later integrate Eric’s edge function to move more processing to the cloud. The updated code can be found here.

My progress is on schedule. By next week, I hope to finalize the locally run code and work on the edge function that should run when a match is uploaded to corroborate with larger models.