Multi-Vehicle Multi-Camera Tracking With Graph-Based Tracklet Features

Nguyen, Tuan T.; Nguyen, Hoang H.; Sartipi, Mina; Fisichella, Marco

doi:10.1109/tmm.2023.3274369

Cited by 8 publications

(1 citation statement)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Graph representation, employing nodes for intersections and edges for routes, provides a holistic view of the entire transportation network. Intelligent agents utilize this graph to track vehicle trajectory [73,74], predict congestion, optimize traffic states, and enhance overall situational awareness by efficiently monitoring the entire network [75][76][77]. This approach enables precise monitoring and analysis of traffic states within the transportation network, facilitating comprehensive situational awareness.…”

mentioning

confidence: 99%

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Kumarasamy,

Saroj,

Liang

et al. 2024

Symmetry

Self Cite

View full text Add to dashboard Cite

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

show abstract

mentioning

confidence: 99%

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Kumarasamy,

Saroj,

Liang

et al. 2024

Symmetry

Self Cite

View full text Add to dashboard Cite

show abstract

LaMMOn: language model combined graph neural network for multi-target multi-camera tracking in online scenarios

Nguyen,

Sartipi

et al. 2024

Mach Learn

View full text Add to dashboard Cite

Multi-target multi-camera tracking is crucial to intelligent transportation systems. Numerous recent studies have been undertaken to address this issue. Nevertheless, using the approaches in real-world situations is challenging due to the scarcity of publicly available data and the laborious process of manually annotating the new dataset and creating a tailored rule-based matching system for each camera scenario. To address this issue, we present a novel solution termed LaMMOn, an end-to-end transformer and graph neural network-based multi-camera tracking model. LaMMOn consists of three main modules: (1) Language Model Detection (LMD) for object detection; (2) Language and Graph Model Association module (LGMA) for object tracking and trajectory clustering; (3) Text-to-embedding module (T2E) that overcome the problem of data limitation by synthesizing the object embedding from defined texts. LaMMOn can be run online in real-time scenarios and achieve a competitive result on many datasets, e.g., CityFlow (HOTA 76.46%), I24 (HOTA 25.7%), and TrackCUIP (HOTA 80.94%) with an acceptable FPS (from 12.20 to 13.37) for an online application.

show abstract

The Power of ANN-Random Forest Algorithm in Human Activities Recognition Using IMU Data

Nia,

Amiri,

Nasab

et al. 2023

2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

View full text Add to dashboard Cite

Multi-Vehicle Multi-Camera Tracking With Graph-Based Tracklet Features

Cited by 8 publications

References 55 publications

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

LaMMOn: language model combined graph neural network for multi-target multi-camera tracking in online scenarios

The Power of ANN-Random Forest Algorithm in Human Activities Recognition Using IMU Data

Contact Info

Product

Resources

About