Abstract. Analyzing trajectories is important and has many applica- tions, such as surveillance, analyzing tra c patterns and hurricane path prediction.

In this paper, we propose a unique, non-parametric trajectory density estimation approach to obtain trajectory density functions that are used for two purposes.

A unique, non-parametric trajectory density estimation approach to obtain trajectory density functions that are used for two purposes, and a density-based ...

In this paper, we propose a unique, non-parametric trajectory density estimation approach to obtain trajectory density functions that are used for two purposes.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev. Hosted as a part of SLEBOK on ...

In this paper, we propose a unique, non-parametric trajectory density estimation approach to obtain trajectory density functions that are used for two purposes.

Apr 12, 2023 · Firstly, spatial–temporal Hausdorff distance is proposed to measure multidimensional information differences of spatiotemporal trajectories, ...

We propose a non-parametric clustering algorithm, which makes little assumptions on prior knowledge of both data distribution and cluster properties. Our ...

Mar 19, 2024 · Abstract: Trajectory computing is a pivotal domain encompassing trajectory data management and mining, garnering widespread attention due to its ...

The approach is based on an unsupervised extension of Density Peak clustering and on a non-parametric density estimator that measures the probability density in ...