Author: rsbaschn

As per last week, the camera we ordered still hasn’t arrived. While concerning, our MVP camera will still serve us well, as even though it doesn’t have IR capabilities or a very high resolution sensor, it is still more than good enough to demonstrate the functionality of the project as a whole.

There are no changes to the overall design of the project. However, we have made a slight change with using AWS Rekognition rather than Sagemaker for verifying low confidence matches. This was done since AWS Rekognition is specifically designed for OCR which is our main use case of the cloud model, which also makes it easier to work with. The costs this incurs are minimal since we did not waste time or resources on the Sagemaker portion other than basic testing. As for schedule, we are currently testing both the ML and camera right now, with ML tests ongoing and camera tests taking place tomorrow before the slides are assembled.

During demos, we verified that we already have a working and functional MVP. The risk is that we need to verify that the replacement camera we bought can work with the system properly, and testing for this will begin as soon as the replacement arrives. However, this is a small risk, as the previous one functioned already, and our backup is sufficient for demonstrating functionality. 

There are no changes to the design, as we are in the final stages of the project. Only testing and full Sagemaker integration remain. 

On the validation side, we plan to test the fully integrated system once we receive and install the replacement camera. We’ll run tests using real-world driving conditions and a portable battery setup to simulate actual usage. We will also test in various lighting conditions. 

More specifically, while we have not run comprehensive tests yet, our initial testing on the timing requirements as well as the database matching are all meeting our requirements of 40 seconds and 100% accuracy, respectively. To test these at a more comprehensive level, we will run 30 simulated matches with different images to make sure all are within the timing and match requirements. Once we receive the camera we will use in our final implementation, we will take images of varying distances and weather/lighting conditions, and test the precision and recall of our whole pipeline. These images will also be put into platerecognizer.com, a commercial model, to see if it is our models that need improvement or the camera. The details of these tests are the same as what we have in the design report. Finally, we will either run this system in an actual car, or take a video of a drive with the camera and feed this video into the pipeline to simulate normal use, and make sure it detects all the matches of the video. 

In the last two weeks,  we finalized our MVP and are working on additional features as well as testing.

A significant risk that could jeopardize the success of the project is currently the camera and the pi. We were able to take images, testing baseline functionality, but when we moved our setup, the camera stopped being detected. Though this can probably be resolved, the impact to our timeline will still be felt. If it is the camera, pi, or cable that is the issue, we need to diagnose soon and order replacements from inventory or the web. 

A minor change was made to the system design by replacing the HTTP POST Supabase edge function with event triggers. This change was necessary because the Raspberry Pi already inserts data directly into the possible_matches table, and may result in more complexity compared to the edge function method.

Right now, our locally run code is finalized outside of the camera problems and can be found here. We also have a working prototype for the website for law enforcement to view matches, and can also be found here.

From the feedback received for our design report, our current risks are to figure out the cloud implementation in more detail and have something we can use to integrate with the Edge compute part. Additionally, we need to test the camera to see if it matches what we need as soon as possible. To that end, we have gotten a barebones and minimal but still usable cloud implementation for our case, and Tzen-Chuen will be hooking up the camera in the next couple days.

Currently, there are no changes to the existing design. We forecast changes next week however, as the major components should be integrated and we will begin testing our MVP, likely learning what could be improved. The current schedule is MVP testing next week, with working on new/replacement components/making the requisite changes the week after that. 

Right now, we have the locally run code mostly finished, and it can be found here. We also have made large strides on the cloud side of things, with databases set up.

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.