Abstract
Accurate measurement of honeybee (Apis mellifera) traffic in the vicinity of the hive is critical in systems that continuously monitor honeybee colonies to detect deviations from the norm. BeePIV, the algorithm we describe and evaluate in this article, is a new significant result in our longitudinal investigation of honeybee flight and traffic in electronic beehive monitoring. BeePIV converts frames from bee traffic videos to particle motion frames with uniform background, applies particle image velocimetry to these motion frames to compute particle displacement vector fields, classifies individual displacement vectors as incoming, outgoing, and lateral, and uses the respective vector counts to measure incoming, outgoing, and lateral bee traffic. We evaluate BeePIV on twelve 30-s color videos with a total frame count of 8928 frames for which we obtained the ground truth by manually counting every full bee motion in each frame. The bee motion counts obtained from these videos with BeePIV come closer to the human bee motion counts than the bee motion counts obtained with our previous video-based bee counting methods. We use BeePIV to compute incoming and outgoing bee traffic curves for two different hives over a period of seven months and observe that these curves closely follow each other. Our observations indicate that bee traffic curves obtained by BeePIV may be used to predict colony failures. Our experiments suggest that BeePIV can be used in situ on the raspberry pi platform to process bee traffic videos.
Dataset: The supplementary materials for our article include three video sets for the reader to appreciate how BeePIV processes bee traffic videos. Each set consists of three videos. The first video is an original raw video captured by our deployed BeePi electronic beehive monitoring system; the second video is the corresponding video that consists of the white background particle motion frames extracted from the first video; the third video is the video of displacement vectors extracted by PIV from each pair of consecutive white background frames from the second video. All computation is executed on a raspberry pi 3 model B v1.2 with four cores.