OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets

10/02/2020
by   Javad Amirian, et al.
0

Human Trajectory Prediction (HTP) has gained much momentum in the last years and many solutions have been proposed to solve it. Proper benchmarking being a key issue for comparing methods, this paper addresses the question of evaluating how complex is a given dataset with respect to the prediction problem. For assessing a dataset complexity, we define a series of indicators around three concepts: Trajectory predictability; Trajectory regularity; Context complexity. We compare the most common datasets used in HTP in the light of these indicators and discuss what this may imply on benchmarking of HTP algorithms. Our source code is released on

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 3

05/28/2020

Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction

Methods to quantify the complexity of trajectory datasets are still a mi...
05/15/2022

Uncertainty estimation for Cross-dataset performance in Trajectory prediction

While a lot of work has been done on developing trajectory prediction me...
06/21/2021

Be your own Benchmark: No-Reference Trajectory Metric on Registered Point Clouds

This paper addresses the problem of assessing trajectory quality in cond...
08/15/2019

Comparing Metrics for Robustness Against External Perturbations in Dynamic Trajectory Optimization

Dynamic trajectory optimization is a popular approach for generating opt...
03/12/2018

Geodabs: Trajectory Indexing Meets Fingerprinting at Scale

Finding trajectories and discovering motifs that are similar in large da...
03/14/2022

A Two-Block RNN-based Trajectory Prediction from Incomplete Trajectory

Trajectory prediction has gained great attention and significant progres...
09/14/2015

Benchmarking for Bayesian Reinforcement Learning

In the Bayesian Reinforcement Learning (BRL) setting, agents try to maxi...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.