The UTC Graduate School is pleased to announce that Tuan Nguyen will present Doctoral research titled, Advancing Approaches to Multi-Target Multi-Camera Tracking: Graph-Based Features, Language Integration, Privacy Preservation, and Large Language Agent Frameworks on 03\/06\/2025 at US time: 9:00 AM – 10:30 AM, Germany time: 15:00 – 16:30 in MDRB Conference Room. Everyone is invited to attend.
Computational Science
Chair: Mina Sartipi
Co-Chair:
Abstract:
Here is the extracted text from the image: — Multi-target multi-camera tracking (MTMCT) is a cornerstone of intelligent transportation systems (ITS), enabling comprehensive monitoring and analysis of vehicle movements across distributed camera networks. Although significant progress has been made, fundamental challenges persist in data association, real-time processing, preservation of privacy, and natural language integration. This dissertation introduces innovative frameworks that systematically address these challenges and advance the state-of-the-art in MTMCT. First, we propose a graph-based model that leverages similarity algorithms to enhance cross-camera object association. Our framework achieves state-of-the-art performance with an IDF1 score of 0.8166 on the CityFlow dataset for offline tracking while maintaining real-time capability at 14 FPS for online scenarios. Next, we present *LaMMOn*, an end-to-end framework integrating language models with graph neural networks. *LaMMOn* addresses data scarcity by generating synthetic embeddings, demonstrating competitive results in multiple datasets, including CityFlow (HOTA 76.46%) and TrackCUIP (HOTA 80.94%). To enable privacy-preserving tracking in large-scale deployments, we develop *FLaMMOn*, a federated learning framework incorporating federated elastic weight consolidation (FedCurv) and federated representation learning (FedRep). *FLaMMOn* outperforms centralized approaches with an IDF1 score of 76.04% while ensuring robust privacy guarantees. Finally, we introduce *MACA*, a large language multi-agent model that enables natural language queries for MTMCT (e.g., \u201cTrack black sedans moving from Camera 1 to Camera 3 and 6 between 2 PM and 5 PM\u201d). *MACA* achieves a HOTA score of 66.23% on our newly introduced Refer-CityFlow dataset. The comprehensive solutions presented in this dissertation enhance MTMCT systems through improved accuracy, scalability, privacy preservation, and user interaction capabilities. The proposed frameworks establish new benchmarks in performance while addressing critical real-world deployment challenges, paving the way for more effective and secure intelligent transportation systems.<\/p>\n","protected":false},"excerpt":{"rendered":"
The UTC Graduate School is pleased to announce that Tuan Nguyen will present Doctoral research titled, Advancing Approaches to Multi-Target Multi-Camera Tracking: Graph-Based Features, Language Integration, Privacy Preservation, and Large Language Agent Frameworks on 03\/06\/2025 at US time: 9:00 AM – 10:30 AM, Germany time: 15:00 – 16:30 in MDRB Conference Room. Everyone is invited to attend. Computational Science… <\/p>\n