Name
Title | ||
|---|---|---|
Travel Time-Dependent Maximum Entropy Inverse Reinforcement Learning for Seabird Trajectory Prediction |
Abstract | ||
|---|---|---|
Trajectory prediction is a challenging problem in the fields of computer vision, robotics, and machine learning, and a number of methods for trajectory prediction have been proposed. Most methods generate trajectories that move toward a goal in a straight line (goal-directed) while avoiding obstacles. However, there are not only such goal-directed trajectories but also trajectories that taking detours to reach the goal (non-goal-directed). In this paper, we propose a method of predicting such non-goal-directed trajectories based on the maximum entropy inverse reinforcement learning framework. Our method introduces travel time as a state of the Markov decision process. As a practical example, we apply the proposed method to seabird trajectories measured using global positioning system loggers. Experimental results show that the proposed method can effectively predict non-goal-directed trajectories. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ACPR.2017.20 | 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) |
Keywords | Field | DocType |
Trajectory Prediction,Maximum Entropy Inverse Reinforcement Learning,Markov Decision Process,Animal Behavior Analysis | Line (geometry),Markov process,Computer science,Markov decision process,Algorithm,Global Positioning System,Artificial intelligence,Principle of maximum entropy,Travel time,Trajectory,Robotics | Conference |
ISSN | ISBN | Citations |
2327-0977 | 978-1-5386-3355-7 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tsubasa Hirakawa | 1 | 12 | 10.99 |
| Takayoshi Yamashita | 2 | 377 | 46.83 |
| Ken Yoda | 3 | 0 | 3.38 |
| Toru Tamaki | 4 | 120 | 30.21 |
| fujiyoshi | 5 | 730 | 101.43 |