LaMMOn: An End-to-End Multi-Camera Tracking Solution Leveraging Transformers and Graph Neural Networks for Enhanced Real-Time Traffic Management

Researchers from the University of Tennessee at Chattanooga and Leibniz University Hannover developed LaMMOn, a multi-camera tracking model using transformers and graph neural networks. LaMMOn integrates modules for object detection, tracking, trajectory clustering, and generating object embeddings from text. It addresses challenges in manual labeling and new matching rules, achieving high performance on datasets like CityFlow and TrackCUIP with competitive real-time processing speeds.