K-means ship trajectory clustering algorithm based on trajectory image similarity

Qi Shi; Yaqiong Fan; Danpu Zhang; Huiming Guo; Jia Liu

doi:10.1117/12.2685502

28 July 2023 K-means ship trajectory clustering algorithm based on trajectory image similarity

Qi Shi, Yaqiong Fan, Danpu Zhang, Huiming Guo, Jia Liu

Proceedings Volume 12716, Third International Conference on Digital Signal and Computer Communications (DSCC 2023); 1271605 (2023) https://doi.org/10.1117/12.2685502
Event: Third International Conference on Digital Signal and Computer Communications (DSCC 2023), 2023, Xi'an, China

Abstract

In order to solve the problems of unbalanced sampling of track points in the track point distance-based ship track clustering algorithm, the abnormality of individual track points affecting the clustering effect, and the difficulty of accurately describing the spatial characteristics of ship tracks with the latitude and longitude data of track points, a Kmeans ship track clustering algorithm based on the similarity of track image features is proposed. The algorithm defines a similarity measure based on trajectory image features. The method converts ship trajectory latitude and longitude time series data into ship trajectory image data, extracts trajectory image features using ResNet-50, and uses the Euclidean distance between trajectory image features as a method to measure the similarity of ship trajectories. Using the similarity measure based on the trajectory image features, the ship trajectories are clustered by the Kmeans algorithm. Experimental results show that the proposed algorithm improves the accuracy by 10% over the traditional DTW-based K-centroid clustering algorithm, and can cluster a large number of complex ship trajectories, and the clustering results are consistent with the actual traffic flow.

Citation Download Citation

Qi Shi, Yaqiong Fan, Danpu Zhang, Huiming Guo, and Jia Liu "K-means ship trajectory clustering algorithm based on trajectory image similarity", Proc. SPIE 12716, Third International Conference on Digital Signal and Computer Communications (DSCC 2023), 1271605 (28 July 2023); https://doi.org/10.1117/12.2685502

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KEYWORDS

Artificial intelligence

Feature extraction

Data conversion

Neural networks

Image processing

Aerospace engineering

Data transmission

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