Discovering Heterogeneous Subsequences for Trajectory Classification

03/18/2019
by   Carlos Andres Ferrero, et al.
0

In this paper we propose a new parameter-free method for trajectory classification which finds the best trajectory partition and dimension combination for robust trajectory classification. Preliminary experiments show that our approach is very promising.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/09/2021

Fast discovery of multidimensional subsequences for robust trajectory classification

Trajectory classification tasks became more complex as large volumes of ...
research
05/20/2020

Collision-free Trajectory Planning for Autonomous Surface Vehicle

In this paper, we propose an efficient and accurate method for autonomou...
research
03/10/2023

Robust MADER: Decentralized Multiagent Trajectory Planner Robust to Communication Delay in Dynamic Environments

Communication delays can be catastrophic for multiagent systems. However...
research
08/07/2023

Amortized Global Search for Efficient Preliminary Trajectory Design with Deep Generative Models

Preliminary trajectory design is a global search problem that seeks mult...
research
12/10/2021

Constant rank factorizations of smooth maps

Sonar systems are frequently used to classify objects at a distance by u...
research
05/11/2020

Optimizing Vessel Trajectory Compression

In previous work we introduced a trajectory detection module that can pr...
research
07/27/2023

Likely, Light, and Accurate Context-Free Clusters-based Trajectory Prediction

Autonomous systems in the road transportation network require intelligen...

Please sign up or login with your details

Forgot password? Click here to reset