  The intelligent swarm behavior of social insects (such as ants) springs up in
different environments, promising to provide insights for the study of embodied
intelligence. Researching swarm behavior requires that researchers could
accurately track each individual over time. Obviously, manually labeling
individual insects in a video is labor-intensive. Automatic tracking methods,
however, also poses serious challenges: (1) individuals are small and similar
in appearance; (2) frequent interactions with each other cause severe and
long-term occlusion. With the advances of artificial intelligence and computing
vision technologies, we are hopeful to provide a tool to automate monitor
multiple insects to address the above challenges. In this paper, we propose a
detection and tracking framework for multi-ant tracking in the videos by: (1)
adopting a two-stage object detection framework using ResNet-50 as backbone and
coding the position of regions of interest to locate ants accurately; (2) using
the ResNet model to develop the appearance descriptors of ants; (3)
constructing long-term appearance sequences and combining them with motion
information to achieve online tracking. To validate our method, we construct an
ant database including 10 videos of ants from different indoor and outdoor
scenes. We achieve a state-of-the-art performance of 95.7\% mMOTA and 81.1\%
mMOTP in indoor videos, 81.8\% mMOTA and 81.9\% mMOTP in outdoor videos.
Additionally, Our method runs 6-10 times faster than existing methods for
insect tracking. Experimental results demonstrate that our method provides a
powerful tool for accelerating the unraveling of the mechanisms underlying the
swarm behavior of social insects.
