運転動画における物体重要度推定のためのインタラクショングラフ
Interaction Graphs for Object Importance Estimation in On-road Driving Videos
運転動画内の物体間の相互作用をグラフ畳み込みでモデル化し、各物体がドライバーの意思決定に与える重要度を推定するフレームワークを提案した。
著者: Zehua Zhang, Ashish Tawari, Sujitha Martin, David Crandall
分類: cs.CV, cs.RO
原文アブストラクト
A vehicle driving along the road is surrounded by many objects, but only a small subset of them influence the driver's decisions and actions. Learning to estimate the importance of each object on the driver's real-time decision-making may help better understand human driving behavior and lead to more reliable autonomous driving systems. Solving this problem requires models that understand the interactions between the ego-vehicle and the surrounding objects. However, interactions among other objects in the scene can potentially also be very helpful, e.g., a pedestrian beginning to cross the road between the ego-vehicle and the car in front will make the car in front less important. We propose a novel framework for object importance estimation using an interaction graph, in which the features of each object node are updated by interacting with others through graph convolution. Experiments show that our model outperforms state-of-the-art baselines with much less input and pre-processing.
