Abstract
In this paper, aiming at preserving more details of the original trajectory data, we propose a novel trajectory summarization approach based on trajectory segmentation. The proposed approach consists of five stages. First, the proposed relative distance ratio based abnormality detection is performed to remove outliers. Second, the remaining trajectories are segmented into sub-trajectories using the minimum description length (MDL) principle. Third, the sub-trajectories are combined into groups by considering both spatial proximity, through the use of searching window, and shape restriction. And the sub-trajectories within the same group are resampled to have the same number of sample points. Fourth, a non-local filtering method based on wavelet transformation is performed on each group. Fifth, the filtered sub-trajectories which derived from the same trajectory are linked together to present the summarization result. Experiments show that our algorithm can obtain satisfactory results.
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Acknowledgment
This work has been funded by Natural Science Foundation of China under Grants Nos. 61471261 and 61771335. The author Yuejun Guo acknowledges support from Secretaria dUniversitats i Recerca del Departament dEmpresa i Coneixement de la Generalitat de Catalunya and the European Social Fund.
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Wu, T., Xu, Q., Li, Y., Guo, Y., Schoeffmann, K. (2019). Detail-Preserving Trajectory Summarization Based on Segmentation and Group-Based Filtering. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11296. Springer, Cham. https://doi.org/10.1007/978-3-030-05716-9_33
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