Data Transformer for Anomalous Trajectory Detection

Hsuan Jen Psan, Wen Jiin Tsai

研究成果: Conference contribution同行評審

摘要

Anomaly detection is an important task in many traffic applications. Methods based on deep learning networks reach high accuracy; however, they typically rely on supervised training with large annotated data. Considering that anomalous data are not easy to obtain, we present data transformation methods which convert the data obtained from one intersection to other intersections to mitigate the effort of collecting training data. The proposed methods are demonstrated on the task of anomalous trajectory detection. A General model and a Universal model are proposed. The former focuses on saving data collection effort; the latter further reduces the network training effort. We evaluated the methods on the dataset with trajectories from four intersections in GTA V virtual world. The experimental results show that with significant reduction in data collecting and network training efforts, the proposed anomalous trajectory detection still achieves state-of-the-art accuracy.

原文English
主出版物標題2021 International Conference on Visual Communications and Image Processing, VCIP 2021 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728185514
DOIs
出版狀態Published - 2021
事件2021 International Conference on Visual Communications and Image Processing, VCIP 2021 - Munich, Germany
持續時間: 5 12月 20218 12月 2021

出版系列

名字2021 International Conference on Visual Communications and Image Processing, VCIP 2021 - Proceedings

Conference

Conference2021 International Conference on Visual Communications and Image Processing, VCIP 2021
國家/地區Germany
城市Munich
期間5/12/218/12/21

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