https://github.com/vishwast333/traffixvision

TraffixVision is an advanced video analysis tool for real-time traffic monitoring. It detects lanes, tracks vehicles using YOLOv8, and analyzes their direction and travel time. Ideal for smart cities, it provides real-time metrics, counts vehicles, and outputs annotated videos, aiding in traffic management and road safety.
https://github.com/vishwast333/traffixvision

opencv python yolov8

Last synced: 5 months ago
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TraffixVision is an advanced video analysis tool for real-time traffic monitoring. It detects lanes, tracks vehicles using YOLOv8, and analyzes their direction and travel time. Ideal for smart cities, it provides real-time metrics, counts vehicles, and outputs annotated videos, aiding in traffic management and road safety.

• Host: GitHub
• URL: https://github.com/vishwast333/traffixvision
• Owner: VishwasT333
• License: mit
• Created: 2024-09-04T13:28:22.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-09-04T14:25:29.000Z (almost 2 years ago)
• Last Synced: 2025-02-12T02:09:16.628Z (over 1 year ago)
• Topics: opencv, python, yolov8
• Language: Jupyter Notebook
• Homepage:
• Size: 28.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# TraffixVision

TraffixVision is an advanced video analysis tool for real-time traffic monitoring. It detects lanes, tracks vehicles using YOLOv8, and analyzes their direction and travel time. Ideal for smart cities, it provides real-time metrics, counts vehicles, and outputs annotated videos, aiding in traffic management and road safety.

## Future Prospects

The primary aim of TraffixVision's vehicle counting and direction prediction features is to optimize traffic signal management. In future iterations, the system could be integrated with traffic signals to dynamically adjust the timing based on real-time vehicle counts. For example, once a fixed number of vehicles are detected in a lane, the signal could turn green, allowing those vehicles to pass, thus reducing congestion.

Given that TraffixVision is tailored for Bangalore, India, the system could be fine-tuned to allow vehicles in the left lane to move freely without waiting for a signal, reflecting local traffic rules where left turns are often allowed on red. Additionally, by analyzing the average time vehicles take to travel from one signal to another, TraffixVision could optimize signal timings even further. For instance, if it takes vehicles 10 seconds to reach a signal, the system could adjust the opposite signal(say) to turn green at the 7th second, minimizing wait times and improving traffic flow efficiency.

These future developments would make TraffixVision a powerful tool for adaptive traffic management, helping reduce congestion and improving the overall efficiency of urban traffic systems.