  In multi-object detection using neural networks, the fundamental problem is,
"How should the network learn a variable number of bounding boxes in different
input images?". Previous methods train a multi-object detection network through
a procedure that directly assigns the ground truth bounding boxes to the
specific locations of the network's output. However, this procedure makes the
training of a multi-object detection network too heuristic and complicated. In
this paper, we reformulate the multi-object detection task as a problem of
density estimation of bounding boxes. Instead of assigning each ground truth to
specific locations of network's output, we train a network by estimating the
probability density of bounding boxes in an input image using a mixture model.
For this purpose, we propose a novel network for object detection called
Mixture Density Object Detector (MDOD), and the corresponding objective
function for the density-estimation-based training. We applied MDOD to MS COCO
dataset. Our proposed method not only deals with multi-object detection
problems in a new approach, but also improves detection performances through
MDOD. The code is available: https://github.com/yoojy31/MDOD.
