VisionGuard

VisionGuard is an AI-powered solution designed to identify specific vehicles and license plates from surveillance footage with precision.

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VisionGuard: AI-Powered Vehicle Identification

Inspiration

Our project was inspired by the increasing need for advanced surveillance systems in public safety and law enforcement. With rising concerns about quick getaway vehicles in crimes, there is a pressing demand for tools that can help identify specific cars, vans, and license plates from video footage. We envisioned "VisionGuard" as a tool that leverages artificial intelligence and machine learning to solve this real-world problem, ensuring faster identification of suspects and more effective use of surveillance footage.

What We Learned

Throughout this project, we learned a great deal about:

  • AWS Rekognition Custom Labels: We discovered the powerful potential of Amazon’s machine learning service to create custom object detection models.
  • Data Labeling: Labeling real-world data to build accurate models was crucial. We gained insight into the importance of high-quality training data.
  • Cloud Infrastructure: Working with S3 buckets, Lambda functions, and IAM roles to streamline the process taught us about the seamless integration of different AWS services.
  • Model Training and Evaluation: We delved into how machine learning models are trained and how to improve them through performance metrics and evaluations.

How We Built VisionGuard

  1. AWS Environment Setup: We began by setting up our AWS environment, creating IAM roles, S3 buckets for storage, and configuring Lambda functions for event-driven processing.
  2. Data Preparation and Labeling: For each challenge, we prepared our training datasets:
    • Specific car models and a bike for Challenge 1.
    • A van for Challenge 2.
    • A license plate for Challenge 3.

We labeled each image using AWS Rekognition Custom Labels and created bounding boxes around the vehicles and license plates.

  1. Training Custom Models: After the dataset preparation, we trained custom Rekognition models for each challenge, optimizing their ability to detect vehicles and license plates.
  2. Evaluation: We ran evaluations on our models using AWS-provided scripts to ensure accuracy and precision in detecting the target objects.
  3. Final Integration: Once the models were fine-tuned, we integrated them into a streamlined solution that can be used for real-time identification in surveillance footage.

Challenges We Faced

  • Time Constraints: With limited time during the hackathon, managing training times for machine learning models was a challenge. We had to prioritize model preparation and optimization to ensure the best results.
  • Data Labeling: Manually labeling the images for custom detection required careful attention to detail. Any mistakes in labeling would affect the model’s performance, so we had to be meticulous.
  • Model Performance: Tuning the models to correctly detect all vehicle types and license plates while avoiding false positives was a complex task, particularly with limited training data.

Despite these challenges, we successfully built VisionGuard, and we are excited about its potential to enhance surveillance and security systems. With further development, it could become a key tool in identifying vehicles involved in criminal activity, significantly improving public safety.

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