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

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

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.