Multiple Insect Tracking with Occlusion Sub-tunnels

Automated tracking methods greatly expedite the collection of data for studying social insects. However, due to the frequency of occlusions (due to interactions) and similarity in appearance and motion features, tracking could easily drift to the incorrect object as the affinity model is unable to discern between similar objects. Recently, a method to filter incorrect associations based on areas of occlusion was proposed. This method only filtered based on entrance and exit within a specific occlusion. In this paper, we propose to improve the tracking of multiple insects involving frequent occlusion by modeling the paths of possible movement within the occlusion tunnel which we call occlusion sub-tunnels. Using two datasets consisting of ants and termites, we demonstrate that the method filters 8% more incorrect associations on average resulting in a reduction of ID switches by 19% over all datasets.