Upper Bound Tracker : A Multi-Animal Tracking Solution for Closed Laboratory Settings

When tracking multiple identical objects or animals in video, many erroneous results are implausible right away, because they ignore a fundamental truth about the scene. Often the number of visible targets is bounded. This work introduces a multiple object pose estimation solution for the case that this upper bound is known. It dismisses all detections that would exceed the maximally permitted number and is able to reidentify an individual after an extended period of occlusion including the reappearance in a different place. An example dataset with four freely interacting laboratory mice is additionally introduced and the tracker’s performance demonstrated on it. The dataset contains various conditions ranging from almost no opportunity to hide for the mice to a fairly cluttered environment. The approach is able to significantly reduce the occurrences of identity switches  the error when a known individual is suddenly identified as a different one  compared to other current solut ions.