Vehicle Detection in Traffic Surveillance
Project Overview:
Objective
The “Vehicle Detection in Traffic Surveillance” project aims to create a dataset for training machine learning models to accurately detect and classify vehicles in traffic surveillance footage. This dataset will support traffic management, safety analysis, and the development of intelligent transportation systems.
Scope
This project involves collecting video footage from traffic cameras, drones, and other surveillance sources, and annotating the footage with bounding boxes and vehicle class labels.
Sources
- Traffic Cameras: Gather video footage from traffic cameras installed at intersections, highways, and urban areas.
- Drones: Capture aerial footage using drones equipped with cameras for bird’s-eye view surveillance.
Data Collection Metrics
- Total Traffic Surveillance Video Clips: 10,000 clips
- Traffic Cameras: 7,000
- Drones: 3,000
Annotation Process
Stages
- Vehicle Detection: Annotate each video frame or clip with bounding boxes around vehicles to indicate their positions.
- Vehicle Classification: Label each annotated vehicle with its class, such as car, truck, bus, motorcycle, etc.
- Metadata Logging: Log metadata, including the camera location, date, time, and weather conditions during data capture
Annotation Metrics
- Video Clips with Vehicle Annotations: 10,000
- Vehicle Bounding Boxes: Varies by clip
- Vehicle Class Labels: Varies by clip
- Metadata Logging: 10,000
Quality Assurance
Stages
Annotation Verification: Implement a validation process involving traffic surveillance experts to review and verify the accuracy of vehicle annotations and classifications.
Data Quality Control: Ensure the removal of video clips with poor quality, low resolution, or those with limited visibility due to environmental conditions.
Data Security: Protect sensitive information and adhere to privacy regulations.
QA Metrics
- Annotation Validation Cases: 1,000 (10% of total)
- Data Cleansing: Remove poor-quality or irrelevant video clips
Conclusion
The “Vehicle Detection in Traffic Surveillance” dataset is a critical resource for traffic management and transportation research. With accurately annotated video footage and comprehensive metadata, this dataset empowers the development of advanced vehicle detection and classification models for traffic surveillance systems. It contributes to improved traffic flow analysis, safety measures, and the development of intelligent transportation solutions aimed at reducing congestion and enhancing road safety.
Quality Data Creation
Guaranteed TAT
ISO 9001:2015, ISO/IEC 27001:2013 Certified
HIPAA Compliance
GDPR Compliance
Compliance and Security
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