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OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets
- Source :
- ACCV 2020-15th Asian Conference on Computer Vision, ACCV 2020-15th Asian Conference on Computer Vision, Nov 2020, Kyoto / Virtual, Japan. pp.1-17, Computer Vision – ACCV 2020 ISBN: 9783030695439, ACCV (6)
- Publication Year :
- 2020
- Publisher :
- HAL CCSD, 2020.
-
Abstract
- 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 Github.<br />Comment: ACCV2020
- Subjects :
- FOS: Computer and information sciences
0209 industrial biotechnology
Source code
Computer science
media_common.quotation_subject
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
motion prediction
Context (language use)
02 engineering and technology
Machine learning
computer.software_genre
01 natural sciences
trajectory dataset
020901 industrial engineering & automation
Motion prediction
0103 physical sciences
[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]
benchmarking
Predictability
trajectory forecasting
010306 general physics
media_common
Series (mathematics)
business.industry
Human trajectory prediction
Benchmarking
Key (cryptography)
Trajectory
dataset assessment
Artificial intelligence
business
computer
Subjects
Details
- Language :
- English
- ISBN :
- 978-3-030-69543-9
- ISBNs :
- 9783030695439
- Database :
- OpenAIRE
- Journal :
- ACCV 2020-15th Asian Conference on Computer Vision, ACCV 2020-15th Asian Conference on Computer Vision, Nov 2020, Kyoto / Virtual, Japan. pp.1-17, Computer Vision – ACCV 2020 ISBN: 9783030695439, ACCV (6)
- Accession number :
- edsair.doi.dedup.....f00b1405b3b5dc9164448c76e6189522