Vessel Pattern Recognition Using Trajectory Shape Feature

In the era of big data, analyzing vessels patterns using massive trajectory data has become the main method of mining activity pattern. Trajectory shape feature, as one of the important features of vessel trajectory data, can be used to identify the vessel activity patterns. But most of research only focused on the features such as standard deviation of latitude and longitude, navigation heading to the analysis of vessels trajectories. Therefore, considering the spatial-temporal feature of vessels data, we propose a method based on Sevcik fractal dimension to extract shape feature for identifying vessels activity types. Firstly, we segment the vessel trajectories to form the sub-trajectory according to the speed and temporal threshold. Secondly, we construct the feature vector of trajectory shape using the improved Sevcik fractal dimension algorithm. Then, we select the standard deviation of latitude and longitude and shape features extracted by Sevcik fractal dimension as the comparison features, and observe the performance in K-means and GMM algorithms respectively to verify the effectiveness of shape feature vectors we proposed. Finally, we select the simulation data and two real data sets for experimental analysis. The results show that the shape feature extraction algorithm can extract the shape features of trajectories, and the performance in classification algorithm is better than the standard deviation and Sevcik fractal dimension. So the method we proposed can realize the pattern recognition of vessel and abnormal trajectory analysist.