Abstract
Nowadays, massive data has been brought by the rapid development of technology. When finding whether trajectory to be detected is abnormal under the premise of given normal trajectories, we innovatively propose 1) Seq2Seq model based on LSTM prediction network for trajectory modelling (SL-Modelling), and 2) abnormal trajectory detection method with spatio-temporal and semantic information. Firstly, SL-Modelling is used to obtain sequence-type trajectory models of normal trajectory groups directly for subsequent detection with no need to extract a large number of features manually and adapting to different sequence length. Then we introduce the concept of distance and semantic interest sequence that makes full use of spatio-temporal and semantic information of trajectories. Finally, the similarity between models and trajectory to be detected is calculated to detect abnormal trajectory. The experimental results of publicly available flight data set show that trajectory models obtained are descriptive enough to represent normal trajectory groups well, and the accuracy of modelling is higher than the existing advanced methods. Besides, the detection with spatio-temporal and semantic information has been verified that it has stronger detection ability with higher accuracy and takes less time.
Highlights
With the development of science and technology, the motion trajectory data gradually increases and becomes a significant branch of big data
Considering the learning ability of LSTM prediction network and the data length adaptability of Seq2Seq model, we propose SL-Modelling for normal trajectory modelling
EXPERIMENTS In the experiments, we construct the data set with publicly available flight trajectory data from Flightradar24, and verify the feasibility and related performance of the SL-Modelling for normal trajectory groups and abnormal trajectory detection with spatio-temporal and semantic information proposed in this paper
Summary
With the development of science and technology, the motion trajectory data gradually increases and becomes a significant branch of big data. The mining of trajectory data is extensively used in various fields of the national economy and national defence construction, such as target tracking, behaviour analysis, tourism and navigation. We should have the capability to catch the point that helps us to find out the reasons and other meaningful attributes corresponding. Detecting abnormal trajectory has significant practical value and has become one of the research hot spots [1]. Trajectory data take time, space and non-spatio-temporal attributes as the essential characteristics of moving objects, and reflect the spatial state evolution process of moving objects over time [2]. The existing abnormal trajectory detection studies pay more attention to classification according to
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