歩行者は横断するのか？2D姿勢推定による予測
Is the Pedestrian going to Cross? Answering by 2D Pose Estimation
単眼画像からCNNによる歩行者検出・追跡・2D姿勢推定を組み合わせ、歩行者の道路横断意図を予測する手法を提案し、自然運転条件のデータセットJAADで最先端の性能を示した。
著者: Zhijie Fang, Antonio M. López
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-of-the-art results.
