Abstract
Computational approaches to the analysis of collective behavior in social insects increasingly rely on motion paths as an intermediate data layer from which one can infer individual behaviors or social interactions. Honey bees are a popular model for learning and memory. Previous experience has been shown to affect and modulate future social interactions. So far, no lifetime history observations have been reported for all bees of a colony. In a previous work we introduced a recording setup customized to track up to 4,000 marked bees over several weeks. Due to detection and decoding errors of the bee markers, linking the correct correspondences through time is non-trivial. In this contribution we present an in-depth description of the underlying multi-step algorithm which produces motion paths, and also improves the marker decoding accuracy significantly. The proposed solution employs two classifiers to predict the correspondence of two consecutive detections in the first step, and two tracklets in the second. We automatically tracked ~2,000 marked honey bees over 10 weeks with inexpensive recording hardware using markers without any error correction bits. We found that the proposed two-step tracking reduced incorrect ID decodings from initially ~13% to around 2% post-tracking. Alongside this paper, we publish the first trajectory dataset for all bees in a colony, extracted from ~3 million images covering 3 days. We invite researchers to join the collective scientific effort to investigate this intriguing animal system. All components of our system are open-source.
Highlights
Social insect colonies are popular model organisms for self-organization and collective decision making
A honey bee colony robustly adapts to changing conditions, whether it may be a hole in the hive that needs to be repaired, intruders that need to be fended off, brood that needs to be reared, or food that needs to be found and processed
Instead, decoding errors in simple markers can be mitigated by the proposed tracking solution, leading to a higher final accuracy of the assigned IDs compared to other marker-based systems that do not employ a tracking step
Summary
Social insect colonies are popular model organisms for self-organization and collective decision making. Evaluating how personal experience shapes the emergence of collective behavior and how individual information is communicated to and processed by the colony requires robust identification of individual bees over long time periods. The video recording must be watched once per individual, which, in the case of a bee hive, might be several hundred or thousand times. Detections that could not be decoded can usually not be integrated into the trajectory, effectively reducing the detection accuracy and sample rate In contrast to these solutions, we have developed a system called BeesBook that uses much less expensive recording equipment (Wario et al, 2015). Merging tracklets after occlusions can be done by matching fingerprints It remains untested whether these approaches can resolve the numerous ambiguities in long-term observations of many hundreds or thousands of bees that may leave the hive for several hours. Instead, decoding errors in simple markers can be mitigated by the proposed tracking solution, leading to a higher final accuracy of the assigned IDs compared to other marker-based systems that do not employ a tracking step
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