  Tracking large numbers of densely-arranged, interacting objects is
challenging due to occlusions and the resulting complexity of possible
trajectory combinations, as well as the sparsity of relevant, labeled datasets.
Here we describe a novel technique of collective tracking in the model
environment of a 2D honeybee hive in which sample colonies consist of
$N\sim10^3$ highly similar individuals, tightly packed, and in rapid, irregular
motion. Such a system offers universal challenges for multi-object tracking,
while being conveniently accessible for image recording. We first apply an
accurate, segmentation-based object detection method to build initial short
trajectory segments by matching object configurations based on class, position
and orientation. We then join these tracks into full single object trajectories
by creating an object recognition model which is adaptively trained to
recognize honeybee individuals through their visual appearance across multiple
frames, an attribute we denote as pixel personality. Overall, we reconstruct
~46% of the trajectories in 5 min recordings from two different hives and over
71% of the tracks for at least 2 min. We provide validated trajectories
spanning 3000 video frames of 876 unmarked moving bees in two distinct colonies
in different locations and filmed with different pixel resolutions, which we
expect to be useful in the further development of general-purpose tracking
solutions.
