Risks
- We have identified a new risk related to our power source. During our weekly meeting, we discussed using a UPS to address the circuit issue in the dash cam system. We need to finalize which UPS solution to adopt, since it must be compatible with the RPi5 and ideally support power input from a car’s 12V cigarette lighter. We also decided that, as a fallback, we could mitigate sudden power loss by installing a manual switch to perform a clean shutdown. This ensures the RPi can complete its shutdown process and minimize the risk of data corruption.
Changes
- We re-evaluated the scope of our ALPR accuracy. Previously, we considered accuracy on a per-frame (per-image) basis, but through experimentation, we realized that assessing accuracy per car provides a more practical measurement. If an average of three images is captured for a single vehicle, we consider it a missed detection only when all three images fail to detect the plate correctly. Shifting to a “per-car” approach more accurately reflects real-world conditions and helps us measure performance in a way that aligns better with our actual use case.
- We updated the use-case requirement of ALPR accuracy from 90% to 80%. Based on experimentations and literature review, we find 80% accuracy to be a more realistic requirement for U.S. real-world dataset end-to-end accuracy due to license plates’ varied and complicated designs.
- We modified the image upload flow in the Central Server to use direct S3 uploads instead of routing through API Gateway. Initially, the system validated and forwarded image uploads via API Gateway and AWS Lambda before storing them in S3. However, this approach introduced inefficiencies due to API Gateway’s payload limits, added latency, and increased costs. To optimize performance, we implemented pre-signed S3 URLs, allowing dash cams to upload images directly to S3. When a license plate is detected, the watchlist query layer checks against a DynamoDB-stored watchlist; if a match is found, an AWS Lambda function generates a pre-signed URL and sends it to Blues NoteHub for direct upload. Once the upload completes, an S3 event triggers another Lambda function, logging the match in RDS and sending alerts via SNS. This change removes API Gateway as a bottleneck, improves system efficiency, reduces costs, and enhances security by restricting upload access to authorized clients with temporary URLs.
- We updated the Web App access layer in the Central Server by replacing an EC2-based backend with AWS API Gateway and Lambda functions. Initially, we assumed an EC2 instance would be more cost-effective, but upon further analysis, we found that law enforcement officers access the system from multiple geographic locations, requiring a more scalable solution. API Gateway now serves as the system’s entry point, with Lambda functions dynamically handling officer requests, interacting with DynamoDB for watchlist storage and RDS for match history retrieval. This adjustment improves scalability by automatically adjusting to traffic loads without manual intervention. While API Gateway introduces some cost, it remains minimal, estimated at $3.50 per million requests, and offers greater flexibility compared to EC2. Given that officer interactions are a small portion of total traffic, this approach balances scalability and cost-effectiveness while ensuring efficient and reliable access to the system.
Schedule
Special Questions
A was written by Andy, B was written by Vicky, and C was written by Christine.
- Global Factors: Our crowdsourced ALPR (Automatic License Plate Recognition) system is designed with global factors in mind to ensure its effectiveness and ethical use across different regions. One of the primary considerations is legal and regulatory compliance. Laws governing ALPR technology vary significantly across countries and even within regions. To address these concerns, we are implementing privacy-focused features, such as not storing license plate information and our data retention policy, ensuring compliance with different legal frameworks. Another key global factor is technological accessibility and infrastructure. The system needs to function reliably in various environments, from developed cities to other areas with limited connectivity. To accommodate this, we are designing edge-processing capabilities so that the dash cameras can locally process images and only upload essential metadata, reducing bandwidth usage. Additionally, the license plate recognition system is trained with real world data that will be optimized to work in diverse conditions, from bright urban environments to low-light or rainy conditions in rural settings.
- Cultural Factors: On one hand, PlatePatrol harnesses community empowerment and civic engagement by transforming everyday dash cams into a crowdsourced public safety network, resonating with America’s tradition of grassroots participation and innovation. On the other hand, the continuous collection and processing of vehicle data raise deep-seated privacy concerns and fears of surveillance in a society that values individual freedom, while historical issues of biased enforcement heighten anxieties about disproportionate targeting of minority and economically disadvantaged communities.
- Environmental Factors: The PlatePatrol system promotes environmental sustainability by leveraging existing dash cams instead of deploying new fixed surveillance cameras, reducing hardware waste and energy consumption. It utilizes AWS Lambda for serverless computing, ensuring resources are used only when needed, minimizing energy waste. Furthermore, by digitizing and streamlining law enforcement workflows, PlatePatrol reduces reliance on paper-based documentation, cutting down waste and promoting an eco-friendly approach to public safety operations.