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Robust bee tracking with adaptive appearance template and geometry-constrained resampling

Studying and analyzing inter-communication among bees requires tracking of many bees. Manual labeling of bees over many frames is painstaking and time-consuming. Automated tracking is challenging because of the appearance change and unreliable features. This problem is magnified when tracking for a longer period of time is required. We present a method for tracking bees that minimizes the accumulation of error over time by using (1) static and adaptive appearance templates for handling appearance change, and (2) geometry-constrained resampling of particles for handling unreliable features. Evaluation against manually-labeled ground truth demonstrates that our method tracks bees with an RMSE of 8.7 pixels (typical bee length is ≫100 pixels), and 75% position and 58% angular error improvement over a particle filtering based tracking with gaussian modeling of appearance.