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AI-based traffic management system using realtime camera feeds

 Department: Computer Science  
 By: usericon olagok01  

 Project ID: 8962
   Rating:  (5.0) votes: 1
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   Price:₦5000
Abstract
The core of the system is a robust software architecture that integrates live video streams from a network of street cameras with a powerful artificial intelligence engine. Utilizing advanced computer vision techniques and deep learning models, specifically Convolutional Neural Networks (CNNs) and object detection algorithms like YOLO, the system performs real-time vehicle detection, classification, and tracking. This enables the continuous monitoring of key traffic metrics, including vehicle count, density, average speed, and lane occupancy rates. Beyond mere monitoring, the system features an adaptive signal control algorithm that dynamically adjusts traffic light phases based on the real-time analyzed data. By prioritizing traffic flow at intersections with higher density and detecting emergent queues, the system minimizes wait times and prevents bottleneck formation. Furthermore, the AI engine is equipped with incident detection capabilities, automatically identifying potential hazards such as accidents, stalled vehicles, or jaywalking pedestrians, and triggering immediate alerts to operators for rapid response. A centralized dashboard provides traffic operators with an intuitive visualization of the entire network, displaying live feeds, analytical summaries, and system alerts. The implementation was validated using simulated and real-world test data, demonstrating a significant reduction in average vehicle wait times and travel delays, alongside improved incident response times. In conclusion, this AI-based traffic management system proves to be a transformative alternative to conventional methods. By harnessing the power of real-time visual data and machine learning, it offers a proactive, data-driven approach to traffic optimization, paving the way for smarter, safer, and more efficient cities. The system is designed with scalability in mind, capable of integrating with future technologies such as connected vehicle ecosystems and city-wide IoT platforms. ...
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