The Probabilistic Object Detection Challenge
This challenge aims to bridge the computer and robotic vision communities by addressing practical robotics applications, though it is incremental as it builds on existing object detection tasks.
The paper introduces the Probabilistic Object Detection Challenge, which extends traditional object detection to include spatial and semantic uncertainty estimates using Gaussian distributions for bounding box corners, and proposes a new probability-based detection quality (PDQ) measure for evaluation.
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.