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Team’s status report for 3/8/25

The most significant risks have remained unchanged, with it being the delay of MVP. These risks are being mitigated by having spare components ordered from ECE inventory to substitute instead of our actual desired components. In terms of contingency plans, there are none, as MVP is a crucial step that cannot be circumvented in any way. However, we are testing the parts of our design as they finish, so we are confident that they will work as we assemble them into an MVP.

We made a few changes to the system design. We updated the cloud reliability target from 95% to 97% to reduce downtime risks and ensure timely database lookups for license plate matching, as AWS’s baseline uptime guarantee is closer to 95%. This is pretty realistic and also follows the published statistics for server uptime on AWS and shouldn’t change our costs. We also refined the edge-to-cloud processing pipeline to improve accuracy and efficiency. Both high and low-confidence detections are sent to the cloud, but low-confidence results also include an image for additional verification using more complex models. This change ensures that uncertain detections receive extra processing while still keeping the system responsive and scalable.

This will not significantly alter the current schedule, as lowering the accuracy requirement will make training easier and potentially quicker.

In addition, we have written the code for using the ML models on the raspberry pi and it can be found here.

Part A (Richard):

Our design should make the world a safer place with regards to child kidnapping. Our device, if deployed at scale, will be able to locate the cars of suspected kidnappers using other cars on the road quickly and effectively, allowing law enforcement to act as fast as possible. While we currently only plan on using the device with amber alerts, a US system, the design should largely work in other countries. The car and license plate detection models are not trained on the cars and plates of any specific country, and PaddleOCR supports multiple languages (over 80) if needed with foreign plates. This means that if other countries have a similar system to amber alerts, they can use our design as well. Our device may also motivate other countries who do not have a similar system to start their own in order to use our design and better find suspected kidnappers in their country.

Part B (Tzen-Chuen):

CALL sits at a conflicting cross-section of cultural values. Generally, our device seeks to protect children, a universal human priority. It accomplishes this through a distributed surveillance network, akin to the saying “it takes a village to raise a child.” By enabling a safer, more vigilant nation, we are in consideration of a global culture.

In terms of traditional “American values,” CALL presents a privacy problem. While not explicitly a constitutional right, it is implied in the 4th amendment. A widespread surveillance network is bound to raise concerns among the general public. We attempt to mitigate this concern by only sending license plate matches that have a certain confidence level to the cloud, and never to the end users. This way we balance the shared cultural understanding of child protection with the American tradition of privacy.

Part C (Eric):

Our solution minimizes environmental impact by leveraging edge computing, which reduces reliance on energy-intensive cloud processing, lowering power consumption and data transmission demands depending on the confidence of the edge model output. The system runs on a vehicle’s 12V power source, eliminating the need for extra batteries and reducing electronic waste. Additionally, its modular design makes it easy to repair and update, extending its lifespan compared to full replacements. These considerations ensure efficient operation while reducing the system’s environmental footprint.

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Richard’s status report for 3/8/25

This week I worked on deploying the ML models to the raspberry pi. This consisted of setting up the python environment, converting the Jupyter notebook into a standard python file, and setting up the file structure the raspberry pi will use. Since the notebook displays the bounding boxes and images when inferencing, when converting to a python file, I removed this code for faster performance since the end user will not see this anyway. I tested this implementation with a sample image that had two license plates in plain view. This is the same image used when testing the Jupyter notebook in Google colab. The program ran in just over 23 seconds, which should be plenty fast enough for our 40 second timing requirement. The models I used were the NCNN models but no quantization was used, so this number can be easily lowered further if needed. The code can be found here. When setting up the file system, I put the pictures and models into their own folders to easily switch between models and the images I test. Last week I worked on the design report, where I focused on the system implementation as well as the design trade studies.

My progress is on schedule. By next week I hope to finalize the MVP of the dashcam side of things, and shift focus to setting up the cloud

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Tzen-Chuen’s Status Report for 3/8

This week I configured the raspberry pi. This included installing a new headless OS for improved performance, and configuring the IPv4 address to allow for remote ssh. This allows us to set the raspberry pi up on CMU wifi and let us ssh into it to program it from wherever we are. The github repo is now also installed on it and progressing towards basic integration.

I didn’t get to creating a table and linking supabase to a user interface yet, but I have been looking into setting it up on a github domain. Now instead of zero cameras we have two cameras, and one cable to test the feedback from the other similar group that we may not have the requisite resolution.

Another significant part of the work this past week was the design report. I handled the Introduction and Project management sections, along with the Testing and Verification sections. While the design report seemed simple on its surface, actually putting every idea down in writing had a very clarifying effect on the overall direction of the project.

Progress is on schedule and now that all major components are here progress will go even smoother.

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Eric’s Status Report for 3/8/25

This week, I mainly focused on refining and updating the design report, specifically working on the Use-Case Requirements, Architecture and/or Principle of Operation, and Design Requirements sections. Some specific changes I made are:

  • Architecture and/or Principle of Operation: I refined the block diagram and system interactions, ensuring that the data flow from image capture → edge processing → cloud verification → database matching was clearly structured. I also improved the documentation/planning of how the edge processor filters and sends high and low-confidence detections to the cloud, reducing unnecessary bandwidth use.
  • Design Requirements: The biggest change since the Design presentation was updating the cloud reliability target. After reviewing existing cloud service reliability standards, I adjusted the uptime requirement from 95% to 97% to strike a balance between AWS’s guaranteed availability and real-world reliability needs. This change ensures that the system remains operational and responsive in high-demand scenarios, reducing the likelihood of missed detections due to cloud downtime.

I also worked with Richard to further define how the cloud infrastructure handles license plate matching and how that would be implemented, specifically using Supabase and AWS Sagemaker. My progress is on schedule, and we have begun testing timing on the Rpi. Next week I plan to continue working with Richard on testing the models on the Rpi, and hopefully begin testing using images from the camera module.