Tracking, Recognizing, and Analyzing Flow For Intersection Control : TRAFFIC: Video-based vehicle recognition and counting at intersections.

Sammanfattning

The increasing demand for accurate traffic monitoring in urban areas has prompted design offices to explore artificial intelligence (AI) for vehicle detection and classification. This thesis investigates the application of computer vision and deep learning to automate traffic analysis at intersections using real-world video footage provided by design offices. The dataset consists of multiple video recordings from different urban intersections under varying conditions (e.g., weather, lighting, camera angles). Using a YOLO11-based object detection model, combined with BoT-SORT for tracking, vehicles were detected, classified, and counted across multiple scenarios. Performance was evaluated using standard metrics (counting accuracy, classification accuracy, tracking consistency), and results were compared against both manual counting and traditional traffic analysis tools. The proposed pipeline reaches 94% of human accuracy in ideal conditions and matches human-level performance in most real-world scenarios, while also aligning with state-of-the-art solutions with broader applicability. Key challenges include occlusion, low resolution, and night-time conditions. These findings show that AI-based methods already provide human-level accuracy in traffic analysis and, given their scalability and consistency, are likely to become the preferred solution for large-scale monitoring.