Atlasベンチマーク：人間の動作予測のための自動評価フレームワーク
The Atlas Benchmark: an Automated Evaluation Framework for Human Motion Prediction
人間の軌道予測アルゴリズムを統一的に評価するベンチマークAtlasを提案し、既存手法を比較した結果、初期の物理ベース手法が依然として競争力を持つことを示した。
著者: Andrey Rudenko, Luigi Palmieri, Wanting Huang, Achim J. Lilienthal, Kai O. Arras
分類: cs.RO
原文アブストラクト
Human motion trajectory prediction, an essential task for autonomous systems in many domains, has been on the rise in recent years. With a multitude of new methods proposed by different communities, the lack of standardized benchmarks and objective comparisons is increasingly becoming a major limitation to assess progress and guide further research. Existing benchmarks are limited in their scope and flexibility to conduct relevant experiments and to account for contextual cues of agents and environments. In this paper we present Atlas, a benchmark to systematically evaluate human motion trajectory prediction algorithms in a unified framework. Atlas offers data preprocessing functions, hyperparameter optimization, comes with popular datasets and has the flexibility to setup and conduct underexplored yet relevant experiments to analyze a method's accuracy and robustness. In an example application of Atlas, we compare five popular model- and learning-based predictors and find that, when properly applied, early physics-based approaches are still remarkably competitive. Such results confirm the necessity of benchmarks like Atlas.
