## 4 Preliminary Evaluation on Omnidirectional Bee Traffic Videos  
We made a preliminary, proof-of-concept evaluation of the system's performance on four omnidirectional bee traffic videos with different levels of bee traffic. We took random samples of 30-s timestamped videos from the validation beehives (i.e., the hives from which we acquired the validation images for BEE2_1S and BEE2_2S) to ensure that there was no overlap with the videos from which the training and testing images were obtained. In particular, we took a random sample of 30 videos from the early morning period (06:00-08:00), a random sample of 30 videos from the early afternoon period (13:00-15:00), a random sample of 30 videos from the late afternoon (16:00-18:00), and a random sample of 30 videos from the evening videos (19:00-21:00). All videos were captured in June and July, 2018. We chose these time periods to ensure that we have videos with different levels of omnidirectional bee traffic. According to our beekeeping experience in Logan and North Logan, Utah, in June and July, there is no or low bee traffic early in the morning or late in the evening and higher bee traffic in the afternoon when foragers leave for and return with nectar, pollen, and water. We then randomly chose one video from each sample of 30 videos to obtain one video for each time period. The Supplementary Materials include the four videos and their textual descriptions. Figure 11: The architecture of ConvNet 3, the best performing hand-designed ConvNet on BEE2_2S (see Table 7). Figure 10: The architecture of ConvNet 1, the best performing hand-designed ConvNet on BEE2_1S (see Table 6). We then counted full bee motions, frame by frame, in each video. The number of bee motions in the first frame of each video (Frame 1) was taken to be 0. In each subsequent frame, we counted the number of full bees that made any motion when compared to their positions in the previous frame. A full bee was considered to have made a motion if it was not in a previous frame but appeared in the following frame (e.g., when it flew into the camera's field of view). The Supplementary Materials include four CSV files with frame-by-frame full bee motion counts. The four videos were arbitrarily classified on the basis of the human bee motion counts as no traffic (NT) (NT_Vid.mp4), low traffic (LT) (LT_Vid.mp4), medium traffic (MT) (MT_Vid.mp4), and high traffic (HT) (HT_Vid.mp4). It took us approximately 2 h to count bee motions in NT_Vid.mp4, 4 h in LT_Vid.mp4, 5.5 h in MT_Vid.mp4, and 6.5 h in HT_Vid.mp4, for a total of 18 h for all four videos. We ran our system on each of the four videos. In each video, motion detection was done with MOG2 [40] while motion region classification was done with VGG16, ResNet32, ConvNetGS3, and ConvNetGS4. The Supplementary Materials include the performance videos for each configuration of the system (i.e., MOG2/VGG16, MOG2/ResNet32, MOG2/ConvNetGS3, MOG2/ConvNetGS4). Each video shows the real-time performance of the system on a specific video so that the green rectangle regions show detected motion regions classified as BEE and the blue rectangle regions show detected motion regions classified as NO-BEE. Given a video, the system returns the total number of bee motions detected in it (see Algorithm 1 in Section 2.2.1). Table 8 gives the bee motion counts returned by each configuration of the system and the human bee motion counts. For NT_Vid.mp4, where there is only one bee flying into a hive, the human bee count is 73 full bee motions, which is closely approximated by the MOG2/ResNet32 configuration. The other configurations roughly double the human bee motion count. When a bee is flying, the motion detection algorithm (e.g., MOG2) sometimes detects motion centered on several bee body parts (e.g., a thorax or a wing). In this case, the motion regions necessarily intersect on the same bee, in which case the motion region classifier (e.g., VGG16) is likely to classify some of the intersecting motion regions as BEE, which increases the bee motion counts.