自律ナビゲーションにおける注視誘導型能動知覚のための高速な人間の注意予測
Fast Human Attention Prediction for Fixation-guided Active Perception in Autonomous Navigation
液体ニューラルネットワークとMobileNetV3を用いた軽量な視線走査予測モデルGazeLNNを提案し、計算コストを99.4%削減しつつ最先端の精度を達成。さらに強化学習で訓練したカメラ制御ポリシーに統合し、実機ドローンでの自律ナビゲーションを実証した。
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著者: Fatma Youssef Mohammed, Grzegorz Malczyk, Kostas Alexis
分類: cs.RO, cs.CV
原文アブストラクト
Human visual attention relies on structured scanpaths to efficiently process scenes, yet instilling this behavior into robot autonomy is in its infancy and hindered by the high,computational costs of existing predictive models. To address this, we introduce GazeLNN, a computationally lightweight,scanpath prediction model that leverages Liquid Neural Networks as its recurrent engine and employs MobileNetV3 for feature extraction. Operating auto-regressively, the architecture predicts sequential fixation heatmaps conditioned on the current visual stimulus and fixation history. Despite requiring only 0.61 GFLOPs, GazeLNN achieves state-of-the-art performance on the MIT Low Resolution dataset achieving 0.47 ScanMatch score. It outperforms existing recurrent baselines across diverse evaluation metrics, while reducing computational costs by 99.40% and accelerating inference by up to six times. To investigate the role of human attention modeling in robot autonomy and demonstrate the practical utility of this highly efficient architecture, we integrate GazeLNN into an active camera-robot control policy trained via Reinforcement Learning. This integration enables human-fixation-guided perception during autonomous navigation, validated through successful real-world deployments on an aerial robot.
