Teetool – A Probabilistic Trajectory Analysis Tool
نویسندگان
چکیده
منابع مشابه
Teetool – A Probabilistic Trajectory Analysis Tool
Teetool is a Python package which models and visualises motion patterns found in twoand threedimensional trajectory data. It models the trajectories as a Gaussian process and uses the mean and covariance of the trajectory data to produce a confidence region, an area (or volume) through which a given percentage of trajectories travel. The confidence region is useful in obtaining an understanding...
متن کاملProbabilistic-trajectory segmental HMMs
“Segmental hidden Markov models” (SHMMs) are intended to overcome important speech-modelling limitations of the conventional-HMM approach by representing sequences (or segments) of features and incorporating the concept of trajectories to describe how features change over time. A novel feature of the approach presented in this paper is that extra-segmental variability between different examples...
متن کاملConstrained free space diagrams: a tool for trajectory analysis
We propose a new and powerful tool for the analysis of trajectories, which in particular allows for more temporally aware analyses. Time plays an important role in the analysis of moving object data. For many applications it is neither sufficient to only compare objects at exactly the same times, nor to consider only the geometry of the trajectories. We show how to leverage between these two ap...
متن کاملJavelin Diagrams: A Graphical Tool for Probabilistic Sensitivity Analysis
T demonstrate post hoc robustness of decision problems to parameter estimates, analysts may conduct a probabilistic sensitivity analysis, assigning distributions to uncertain parameters and computing the probability of decision change. In contrast to classical threshold proximity methods of sensitivity analysis, no appealing graphical methods are available to present the results of a probabilis...
متن کاملMTTV - An Interactive Trajectory Visualization and Analysis Tool
We present an interactive visualizer that enables the exploration, measurement, analysis and manipulation of trajectories. Trajectories can be generated either automatically by multi-target tracking algorithms or manually by human annotators. The visualizer helps understanding the behavior of targets, correcting tracking results and quantifying the performance of tracking algorithms. The input ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Open Research Software
سال: 2017
ISSN: 2049-9647
DOI: 10.5334/jors.163