  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.
