Practical Solutions for Multi-Camera Tracking in Intelligent Transportation Systems
Enhancing Traffic Management with LaMMOn
Efficient traffic management has been improved with advancements in computer vision, enabling accurate prediction and analysis of traffic volumes. LaMMOn, an end-to-end multi-camera tracking model, addresses challenges in multi-target multi-camera tracking (MTMCT) by leveraging transformers and graph neural networks.
Key Modules of LaMMOn
LaMMOn integrates three modules: Language Model Detection (LMD) for object detection, Language and Graph Model Association (LGMA) for tracking and trajectory clustering, and Text-to-embedding (T2E) module for generating object embeddings from text to address data limitations.
Performance and Effectiveness
LaMMOn excels in various datasets, achieving competitive results and real-time processing speeds. It eliminates the need for new matching rules and manual labeling by leveraging synthesized embeddings from text, providing practical solutions for multi-camera tracking challenges.
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