Existing object detection algorithms for specific case

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detection

computer-vision

I'm looking for a algorithm (best case in opencv) to detect and track objects in a specific low resolution distance image. The goal is to have a bounding box, which best describes the object. For my use case I only have 16 Regions of Interest. Each RoI gives me the distance of the object in that Region and the amount of space occupied by that object (percent). Here is how the information could look like:

example

Since I only get one distance per RoI there is no way to differentiate between 2 objects inside this RoI by just using this information. But maybe there is an algorithm that can make benefit of the occupancy information. Which algorithms could be used to detect and track multiple objects inside the field of view with the given information.

additional information:
-The pixelspace for the bounding box is at a fixed size of 500,500 (size of outputgrid)
-The algorithm would therefore have to take the 4x4 pixels and operate in subpixelspace to find the bounding boxes
-Objects can not lie on of of each other, but can be beside each other like in the example (row 2, col 0)

What I have tried:

I've looked at several algorithms like the Blob-Detector in opencv. But they all work with normal pixel values instead of the values that I am given and I'm not sure how I could redefine the picture information, so that it can be used with these normal computer vision algorithms.