## 1 Introduction  
Honeybees (_Apis mellifera_) provide approximately 14% of the overall pollination services in agricultural production [1]. Honeybee traffic, i.e., the number of bees that move in close proximity to a beehive per a given time period, is an important indicator of how bee colonies react to foraging opportunities, weather events, pests, and diseases. For example, pollen supply affects brood rearing rates [2] and worker longevity [3], both of which may, in turn, affect honeybee traffic. Many commercial and amateur beekeepers watch honeybee traffic near their hives to ascertain the state of their colonies, because honeybee traffic carries information on colony behavior and phenology. Honeybee traffic is an indicator of colony exposure to stressors such as failing queens, predatory mites, and airborne toxicants. While experienced beekeepers can arguably tell which honeybee traffic patterns are produced by stressed colonies, they may not always be physically present to observe them in time to administer appropriate treatment due to fatigue or problems with logistics. The cost of transportation is also a factor, as many beekeepers drive longer distances to their far flung apiaries due to ever increasing urban and suburban sprawl. Insomuch as beekeepers cannot monitor their hives continuously, electronic beehive monitoring (EBM) can help extract valuable information on honeybee colonies without invasive hive inspections or transportation costs [4; 5]. Deep learning (DL) and computer vision (CV) have so far remained relatively unexplored in EBM [4; 6; 7; 8; 9]). However, several researchers have used both DL and CV in their projects. [PERSON] et al. [10] proposed a system for pose detection and tracking of multiple insects and animals, which they used to monitor the traffic of honeybees and mice. The system uses a deep neural network to detect and associate detected body parts into whole insects or animals. The network predicts a set of 2D confidence maps of present body parts and a set of vectors of part affinity fields that encode associations detected among the parts. Greedy inference is subsequently used to select the most likely predictions for the parts and compiles the parts into larger insects or animals. No details appear to be given on what hive was used in the experiments. From the pictures given in the publication, it appears that the hive used in the experiments is a smaller laboratory hive, not a standard Langstroth or Dadant hive used by many beekeepers worldwide. The system requires a structural modification of the beehive in that a transparent acrylic plastic cover located on top of the ramp ensures the bees remain in the focal plane of the camera. The system uses trajectory tracking to distinguish entering and leaving bees, which works reliably under smaller bee traffic levels. The dataset consists of 100 fully annotated frames, where each frame contains 6-14 honeybees. Since the dataset does not appear to be public, the experimental results cannot be independently replicated. [PERSON] et al. [11] proposed a system for pollen bearing honeybee detection in surveillance videos obtained at the entrance of a hive. The system's hardware includes a specially designed wooden box with a raspberry pi camera module inside. The box is mounted on the front side of a standard hive above the hive entrance. There is a glass plate placed on the bottom side of the box, 2 cm above the flight board, which forces the bees entering or leaving the hive to crawl a distance of \(\approx\)11 cm. Consequently, the bees in the field of view of the camera cannot fly. The training dataset contains 50 images of pollen bearing honeybees and 50 images of honeybees without pollen. The test dataset consists of 354 honeybee images. Since neither dataset appears to be public, the experimental results cannot be independently replicated.