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.
翻译:我们引入了计算机和机器人视觉的新挑战,即第一个ACRV机器人视觉挑战,即概率物体探测。概率物体探测是传统物体探测任务的新变化,需要空间和语义不确定性的估计。我们扩展了传统的物体探测捆绑框格式,以利用箱角的百日咳分布来表达空间不确定性。挑战引入了新的视频序列测试数据集,该数据集旨在更接近机器人系统可用的数据类型。我们利用基于概率的探测质量(PDQ)新措施评估概率探测。创建这一挑战的目标是将计算机和机器人视觉群聚集在一起,为实际机器人应用应用对象探测解决方案。