単眼視による人物位置推定と軌道予測の統合フレームワーク
Unified Human Localization and Trajectory Prediction with Monocular Vision
単眼カメラ映像のみから、Transformerを用いて人物の位置推定と将来の軌道予測を同時に行う手法を提案し、雑音の多い実環境でも頑健に動作することを示した。
著者: Po-Chien Luan, Yang Gao, Celine Demonsant, Alexandre Alahi
分類: cs.CV, cs.RO
原文アブストラクト
Conventional human trajectory prediction models rely on clean curated data, requiring specialized equipment or manual labeling, which is often impractical for robotic applications. The existing predictors tend to overfit to clean observation affecting their robustness when used with noisy inputs. In this work, we propose MonoTransmotion (MT), a Transformer-based framework that uses only a monocular camera to jointly solve localization and prediction tasks. Our framework has two main modules: Bird's Eye View (BEV) localization and trajectory prediction. The BEV localization module estimates the position of a person using 2D human poses, enhanced by a novel directional loss for smoother sequential localizations. The trajectory prediction module predicts future motion from these estimates. We show that by jointly training both tasks with our unified framework, our method is more robust in real-world scenarios made of noisy inputs. We validate our MT network on both curated and non-curated datasets. On the curated dataset, MT achieves around 12% improvement over baseline models on BEV localization and trajectory prediction. On real-world non-curated dataset, experimental results indicate that MT maintains similar performance levels, highlighting its robustness and generalization capability. The code is available at https://github.com/vita-epfl/MonoTransmotion.
