The Probabilistic Object Detection Challenge

Autor:	Skinner, John, Hall, David, Zhang, Haoyang, Dayoub, Feras, Sünderhauf, Niko
Rok vydání:	2019
Předmět:	Computer Science - Robotics Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 4 pages, workshop paper
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1903.07840 Zobrazit plný text záznamu View this record from Arxiv