  From human crowds to cells in tissue, the detection and efficient tracking of
multiple objects in dense configurations is an important and unsolved problem.
In the past, limitations of image analysis have restricted studies of dense
groups to tracking a single or subset of marked individuals, or to
coarse-grained group-level dynamics, all of which yield incomplete information.
Here, we combine convolutional neural networks (CNNs) with the model
environment of a honeybee hive to automatically recognize all individuals in a
dense group from raw image data. We create new, adapted individual labeling and
use the segmentation architecture U-Net with a loss function dependent on both
object identity and orientation. We additionally exploit temporal regularities
of the video recording in a recurrent manner and achieve near human-level
performance while reducing the network size by 94% compared to the original
U-Net architecture. Given our novel application of CNNs, we generate extensive
problem-specific image data in which labeled examples are produced through a
custom interface with Amazon Mechanical Turk. This dataset contains over
375,000 labeled bee instances across 720 video frames at 2 FPS, representing an
extensive resource for the development and testing of tracking methods. We
correctly detect 96% of individuals with a location error of ~7% of a typical
body dimension, and orientation error of 12 degrees, approximating the
variability of human raters. Our results provide an important step towards
efficient image-based dense object tracking by allowing for the accurate
determination of object location and orientation across time-series image data
efficiently within one network architecture.
