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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.

The tracking of ants in video is important for the analysis of their complex group behavior. However, the manual analysis of these videos is tedious and time consuming. Automated tracking methods tend to drift due to frequent occlusions during their interactions and similarity in appearance. Semi-automated tracking methods enable corrections of tracking errors by incorporating user interaction. Although it is much lower than manual analysis, the required user time of the existing method is still typically 23 times the actual video length. In this paper, we propose a new semi-automated method that achieves similar accuracy while reducing the user interaction time by (1) mitigating user wait time by incorporating a data association tracking method to separate the tracking from user correction, and (2) minimizing the number of candidates visualized for user during correction. This proposed method is able to reduce the user interaction time by 67% while maintaining the accuracy within 3% of the previous semi-automated method [11].

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