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 applicability of automated motion analysis is immense and continues to grow as our ability to record objects of interest becomes easier and less expensive. In the case of multi-object tracking, data association methods have been proposed to improve handling of occlusions. These methods are strongly affected by their ability to measure association affinities between fragmented object trajectories. Obtaining labeled training examples for learning how to measure these associations can be expensive and time-consuming. We propose an interactive training framework that utilizes an uncertainty based active sampling approach in combination with semi-supervised learning in order to reduce the number of labeled examples needed for training. Additionally, an affinity scoring function is learned with Random Forest to speed up learning affinity measures in order to make the interactive training framework possible. Experimental results on two 10,000 frame video sequences of ant colonies demonstrates a significant reduction in the amount of labeled examples needed over random sampling.