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In order to understand the behavior of adult Drosophila melanogaster (fruit flies), vision-based 3D trajectory reconstruction methods are adopted. To improve the statistical strength of subsequent analysis, high-throughput measurements are necessary. However, ambiguities in both stereo matching and temporal tracking appear more frequently in high density situations, aggravating the complexity of the 3D tracking situation. In this paper we propose a high density object tracking algorithm. Instead of approximating trajectories for all frames in a direct manner, in ambiguous situations, tracking is terminated to generate robust tracklets based on the modified tracking-by-matching method. The terminated tracklets are linked to ongoing (unterminated) tracklets with minimum linking cost in an on-line fashion. Furthermore, we introduce a set of new evaluation metrics to analyze the tracking results. These metrics are used to analyse the effect of detection noise and compare our tracking algorithm with two state-of-the-art 3D tracking methods based on simulated data with hundreds of flies. The results indicate that our proposed algorithm outperforms both, the tracking-by-matching algorithm and a global correspondence selection approach.

Stereo and Motion Based 3D High Density Object Tracking