Abstract
Vehicle behavior analysis is often based on the motion trajectory analysis, which lays the foundation for many applications such as velocity detection, vehicle classification, and vehicle counting. In this paper, a trajectory clustering framework is proposed for vehicle trajectory analysis. Firstly, feature points are extracted by ORB algorithm which uses binary strings as an efficient feature point descriptor. Secondly, a matching method based on Hamming distance is used to obtain the tracking trajectory points. Finally, a novel clustering method, which contains three phrases, i.e., coarse clustering, fine clustering, and agglomerative clustering, is proposed to classify vehicle trajectory points based on the 3D information in real traffic video. By applying this clustering method in actual traffic scenes, much more stable clustering results can be obtained compared with other methods. Experimental results demonstrate that the accuracy of the proposed method can reach 95%. Furthermore, vehicle type can be estimated to realize vehicle classification.
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Acknowledgements
This work is supported by the National Natural Science Fund of China (No. 61572083), the Natural Science Foundation of Shaanxi Province (Nos. 2015JQ6230, 2015JZ018), and the Fundamental Research Funds for the Central Universities of China (Nos. 310824163411, 310824171003).
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Communicated by M. Anisetti.
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Song, H., Wang, X., Hua, C. et al. Vehicle trajectory clustering based on 3D information via a coarse-to-fine strategy. Soft Comput 22, 1433–1444 (2018). https://doi.org/10.1007/s00500-017-2831-0
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DOI: https://doi.org/10.1007/s00500-017-2831-0