遠近感を考慮した道路障害物検出
Perspective Aware Road Obstacle Detection
画像内の見かけの大きさを表すスケールマップを計算し、遠近感に応じた合成物体で学習データを生成するとともに検出ネットワークのデコーダに遠近情報を組み込むことで、道路障害物検出精度を向上させた。
著者: Krzysztof Lis, Sina Honari, Pascal Fua, Mathieu Salzmann
分類: cs.CV, cs.RO
原文アブストラクト
While road obstacle detection techniques have become increasingly effective, they typically ignore the fact that, in practice, the apparent size of the obstacles decreases as their distance to the vehicle increases. In this paper, we account for this by computing a scale map encoding the apparent size of a hypothetical object at every image location. We then leverage this perspective map to (i) generate training data by injecting onto the road synthetic objects whose size corresponds to the perspective foreshortening; and (ii) incorporate perspective information in the decoding part of the detection network to guide the obstacle detector. Our results on standard benchmarks show that, together, these two strategies significantly boost the obstacle detection performance, allowing our approach to consistently outperform state-of-the-art methods in terms of instance-level obstacle detection.
