雷达学报2019，Vol.8Issue(1)：36-43,8.
基于Bi-LSTM模型的轨迹异常点检测算法
Trajectory Outlier Detection Algorithm Based on Bi-LSTM Model
摘要
Abstract
The rapid advances in positioning technology have created huge spatio-temporal trajectory data, and there are always obvious aberrant outliers in trajectory data. Detecting outliers in the trajectory is critical to improving data quality and the accuracy of subsequent trajectory data mining tasks. In this paper, we propose a trajectory outlier detection algorithm based on a Bidirectional Long Short-Term Memory (Bi-LSTM) model. First, a six-dimensional motion feature vector is extracted for each trajectory point, and then we construct a Bi-LSTM model. The model input is the trajectory data feature vector of a certain sequence length, and its output is the class type of the current track point. In addition, a combination method of undersampling and oversampling is applied to mitigate the effect of data distribution imbalance on detection performance. The Bi-LSTM model can automatically learn the difference between the normal points and adjacent abnormal points in the motion characteristics by combining the LSTM unit and the bidirectional network. Experimental results based on a real ship trajectory annotation data show that the detection performance of our proposed algorithm significantly exceeds those of the constant velocity threshold algorithm, non-sequential classical machine learning classification algorithms, and convolutional neural network model. Especially, the recall value of the proposed algorithm reaches 0.902, which verifies its effectiveness.关键词
轨迹数据/ 异常检测/ 特征提取/ 双向长短时记忆网络/Key words
Trajectory data/ Outlier detection/ Feature extraction/ Bidirectional Long Short-Term Memory (Bi-LSTM) networks/分类
信息技术与安全科学引用本文复制引用
韩昭蓉,黄廷磊,任文娟,许光銮..基于Bi-LSTM模型的轨迹异常点检测算法[J].雷达学报,2019,8(1):36-43,8.基金项目
国家自然科学基金(61725105, 61331017) （61725105, 61331017）
