.png){kind=icon}
返回
A Spatio-Temporal Track Association Algorithm Based on Marine Vessel Automatic Identification System Data
.png){kind=icon}
OA
AI
DOI：10.1109/TITS.2022.3187714.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Tracking multiple moving objects in real-time in a dynamic threat environment is an important element in national security and surveillance system. It helps pinpoint and distinguish potential candidates posing threats from other normal objects and monitor the anomalous trajectories until intervention. To locate the anomalous pattern of movements, one needs to have an accurate data association algorithm that can associate the sequential observations of locations and motion with the underlying moving objects, and therefore, build the trajectories of the objects as the objects are moving. In this work, we develop a spatio-temporal approach for tracking maritime vessels as the vessel's location and motion observations are collected by an Automatic Identification System. The proposed approach is developed as an effort to address a data association challenge in which the number of vessels as well as the vessel identification are purposely withheld and time gaps are created in the datasets to mimic the real-life operational complexities under a threat environment. Three training datasets and five test sets are provided in the challenge and a set of quantitative performance metrics is devised by the data challenge organizer for evaluating and comparing resulting methods developed by participants. When our proposed track association algorithm is applied to the five test sets, the algorithm scores a very competitive performance.
Keyword:
Artificial intelligence
Trajectory
Training
Tracking
Marine vehicles
Seaports
Radar tracking
AIS
online clustering
threat detection
track association
trajectory tracking
期刊
IF:
8.4
论文数:
9.7K
被引数:
6.3W
机构
引用论文
Effect of data environment and cognitive ability on participants’ attitude towards data governance
PREAI
分享收藏分享收藏AIS-Based Intelligent Vessel Trajectory Prediction Using Bi-LSTM基于AIS的bi-lstm智能船舶轨迹预测.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Robust Data Association Using Fusion of Data-Driven and Engineered Features for Real-Time Pedestrian Tracking in Thermal Images融合数据驱动和工程特征的鲁棒数据关联，用于热图像中的实时行人跟踪SENSORS
OAAI
SENSORSIF3.5
OAAI分享收藏
查看更多内容