Traffic Safety AI
Prudent Partners annotated traffic footage to identify violations, collisions, and pedestrian movement for a smart city initiative. The video annotation helped improve road safety enforcement and AI-driven incident detection.
Case Details
Clients: Pixel Art Company
Start Day: 13/01/2024
Tags: Marketing, Business
Project Duration: 9 Month
Client Website: Pixelartteams.com
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Executive Summary
An AI-driven traffic surveillance system was developed to automate detection of road safety violations and accident-prone scenarios. The project centered on high-quality visual annotations for license plates, helmet and seatbelt use, and accident zones to enhance the model’s detection accuracy, reduce false readings, and accelerate deployment timelines.
Introduction
Background
This initiative addressed increasing urban traffic violations and safety risks. The system processes image and video data to identify non-compliance with safety norms and detect hazards in real time.
Industry
Smart Transportation / Computer Vision / Traffic Enforcement
Challenge
Problem Statement
Initial datasets contained mislabeling, inconsistent bounding boxes, poor license plate recognition, and under-annotated accident scenes.
Impact
- High false positives/negatives in detecting helmets and seatbelts
- OCR mismatches due to incorrect license plate bounding
- Missed road hazards and accident indicators
- Slower annotation cycles delaying training and deployment
Solution
Overview
A structured annotation pipeline was implemented to ensure consistency and completeness across safety-related visual elements.
Approach
Helmet & Seatbelt Annotation:
- Standardized labels: helmet, no_helmet, seatbelt, no_seatbelt
- Focused on correct bounding and visibility for each person on the vehicle
License Plate Recognition:
- OCR applied to plate regions
- Files renamed using actual alphanumeric values extracted from plates
- Standard naming conventions applied for traceability
Accident Zone Identification:
- Labeled accident_scene, obstacle, hazard, and large_materials
- Focused on identifying fallen objects, collisions, and stalled vehicles
Quality Assurance:
- SOPs with decision-tree logic for ambiguous cases (e.g., tilted helmets)
- Internal checks and team reviews to validate annotations
- Batch-wise consistency verification and error correction
Results
Outcome
The project produced high-quality, structured datasets, improving the reliability of AI models in identifying traffic violations and safety risks.
Benefits
- Higher Accuracy: Fewer false alarms for helmet and seatbelt usage
- Improved OCR Integration: Better license plate alignment with image metadata
- Efficient Workflow: Faster annotation due to clarity in guidelines
- Faster Deployment: Clean datasets enabled rapid model iterations
Conclusion
Summary
Structured annotation is critical in developing dependable AI systems for traffic safety. The integration of OCR, object detection, and hazard labeling in a unified annotation framework led to measurable improvements in detection performance and operational efficiency.
Future Plans
- Expand detection to include distracted driving and phone usage
- Incorporate object tracking for accident sequence analysis
- Enable real-time alerts from live video feeds for smart city deployments
Call to Action
Agencies involved in traffic monitoring and urban planning can replicate this structured annotation framework to improve compliance tracking and enhance road safety through AI.