Ant Tracking with Occlusion Tunnels

Abstract

The automated tracking of social insects, such as ants, can efficiently provide unparalleled amounts of data for the of study complex group behaviors. However, a high level of occlusion along with similarity in appearance and motion can cause the tracking to drift to an incorrect ant. In this paper, we reduce drifting by using occlusion to identify incorrect ants and prevent the tracking from drifting to them. The key idea is that a set of ants enter occlusion, move through occlusion then exit occlusion. We do not attempt to track through occlusions but simply find a set of objects that enters and exits them. Knowing that tracking must stay within a set of ants exiting a given occlusion, we reduce drifting by preventing tracking to ants outside the occlusion. Using four 5000 frame video sequences of an ant colony, we demonstrate that the usage of occlusion tunnel reduces the tracking error of (1) drifting to another ant by 30% and (2) early termination of tracking by 7%.