Mining spatial–temporal motion pattern for vessel recognition

Lu Sun,Jian Guan,Wei Zhou,You He

doi:10.1177/1550147718779563

Lu Sun, Jian Guan + Show 2 more

Open Access

https://doi.org/10.1177/1550147718779563

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Abstract

Approaches of vessel recognition are mostly accomplished by sensing targets and extracting target features, without taking advantage of spatial and temporal motion features. With maritime situation management systems widely applied, vessels’ spatial and temporal state information can be obtained by many kinds of distributed sensors, which is easy to achieve long-time accumulation but are often forgotten in databases. In order to get valuable information from large-scale stored trajectories for unknown vessel recognition, a spatial and temporal constrained trajectory similarity model and a mining algorithm based on spatial and temporal constrained trajectory similarity are proposed in this article by searching trajectories with similar motion features. Based on the idea of finding matching points between trajectories, baseline matching points are first defined to provide time reference for trajectories at different time, then the almost matching points are obtained by setting the spatial and temporal constraints, and the similarity of pairwise almost matching points is defined, which derives the spatial and temporal similarity of trajectories. By searching the matching points from trajectories, the similar motion pattern is extracted. Experiments on real data sets show that the proposed algorithm is useful for similar moving behavior mining from historic trajectories, which can strengthen motion feature with the length increases, and the support for vessel with unknown property is larger than other models.

Highlights

Target recognition is well known as one of the key functions in a distributed multisensor information fusion system
Two real data sets are used for the experiment, which include an automatic identification system (AIS) data set as the historical motion pattern reference and a maritime radar data set as the test data with identity under confirmed
Since the proposed spatial and temporal constrained trajectory similarity (STCTS)-based mining algorithm uses spatial threshold, temporal threshold, and similarity threshold, in order to uniform the conditions of experiments, the parameters of the proposed method in the comparative experiment are set the same as the experiments in section ‘‘Experiment results.’’ The spatial threshold in similar trajectory mining methods based on modified Hausdorff distance (MHD), interpolated modified Hausdorff distance (IMHD), and longest common subsequence (LCSS) model is set to the value corresponding to the similarity threshold, respectively

Summary

Introduction

Target recognition is well known as one of the key functions in a distributed multisensor information fusion system. Given a spatial threshold e, a temporal threshold t, and pi, k 2 Ti is a data point on trajectory Ti, Mj Tj is the match points set for pi, k on trajectory Tj. We call pj, ‘ 2 Mj is the optimal matching point of pi, k if pj, ‘ satisfies scoree, t(pi, k , pj, ‘) = maxffe(pi, k, pj, s)jpj, s 2 Mjg, and scoree, t(pi, k, pj, ‘) is the similarity measure of \pi, k, pj, ‘. Given a spatial threshold e, a temporal threshold t, pi, k 2 Ti is a data point on trajectory Ti, and pj, ‘ is the optimal matching point of pi, k on trajectory Tj. The spatial–temporal similarity between pi, k and Tj is defined as Se, t(pi, k, Tj) = scoree, t(pi, k, pj, ‘). As long as Ti and Tj are similar enough, TSe, t(Ti, Tj) and TSe, t(Tj, Ti) are enough to represent the similarity of Ti and Tj

Experiment and discussion

Conclusion