Visual Analytics for Detecting Illegal Transport Activities
Yi Shan - Fudan University, Shanghai, China
Aolin Guo - Fudan University, Shanghai, China
Zekai Shao - Fudan University, Shanghai, China
Tian Qiu - Fudan University, Shanghai, China
Xueli Shu - Fudan University, Shanghai, China
Qianhui Li - Fudan University, Shanghai, China
Siming Chen - Fudan University, Shanghai, China
Room: Bayshore II
Sunday, October 13th, 2024 @ 12:30GMT+00:00Change your timezone on the schedule page
6 months agoYour current time: Tuesday, Apr 8th @ 22:14
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Abstract
This paper presents a visual analytics system designed to address the IEEE VAST Challenge 2024 Mini-Challenge 2. The system can support the matching and anomaly detection of multi-source heterogeneous spatio-temporal data, thereby enabling the detection of illegal transport activities. The primary contribution of the system lies in its analysis-driven interaction design.