確率的物体検出チャレンジ
The Probabilistic Object Detection Challenge
ロボティクス向けに空間・意味的不確かさを推定する確率的物体検出の新ベンチマークと評価指標PDQを提案した論文。
著者: John Skinner, David Hall, Haoyang Zhang, Feras Dayoub, Niko Sünderhauf
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
We introduce a new challenge for computer and robotic vision, the first ACRV Robotic Vision Challenge, Probabilistic Object Detection. Probabilistic object detection is a new variation on traditional object detection tasks, requiring estimates of spatial and semantic uncertainty. We extend the traditional bounding box format of object detection to express spatial uncertainty using gaussian distributions for the box corners. The challenge introduces a new test dataset of video sequences, which are designed to more closely resemble the kind of data available to a robotic system. We evaluate probabilistic detections using a new probability-based detection quality (PDQ) measure. The goal in creating this challenge is to draw the computer and robotic vision communities together, toward applying object detection solutions for practical robotics applications.
