# Audio, Image, Video, and Weather Datasets for Continuous Electronic Beehive Monitoring
## 4 Video Datasets  
In 2020, we started applying concepts of particle image velocimetry (PIV) to the analysis of honeybee traffic videos [10]. Our first algorithm used PIV to compute motion vectors, classified them as incoming, outgoing, or lateral by vector direction, and returned the classified vector counts as measurements of directional traffic levels. To evaluate this algorithm, we created our first video dataset, which we called BEE_VID1, that consists of four 30-s videos. We took \(\approx\)3500 timestamped 30-s bee traffic videos acquired by two deployed BeePi monitors in Logan, UT in June and July 2018. Each video had 744,640 \(\times\) 480 frames. From this collection, we took four random samples of 30 videos each: (1) a sample from the early morning (06:00-08:00); (2) a sample from the early afternoon (13:00-15:00); (3) a sample of videos from the late afternoon (16:00-18:00); (4) a sample from the evening (19:00-21:00). From each of the four video samples, we randomly selected one video. Thus, we acquired one early morning video, one early afternoon video, one late afternoon video, and one evening videos. The total frame count for the four videos is 2976 frames. We labeled the first video as no traffic (NT_VID), the second video as medium traffic (MT_VID), the third video as high traffic (HT_VID), and the fourth video as low traffic (LT_VID), which reflects the general bee traffic patterns we have observed in multiple videos acquired with different BeePi monitors at different apiaries in northern Utah in 2018-2020. For each of these four videos, we manually counted full bee motions, frame by frame, in each video. A full bee motion is the change of position of a complete honeybee body in a given frame taken at time \(t>1\) (i.e., \(F_{t}\)) relative to the previous frame taken at time \(t-1\) (i.e., \(F_{t-1}\)). The number of bee motions in the first frame of each video \(F_{1}\) is taken to be 0. In each subsequent frame, we manually counted the number of full bees that made any motion when compared to their positions in the previous frame. We also counted as full bee motions complete bee bodies appearing in \(F_{t}\) and not present in \(F_{t-1}\) (e.g., when a bee flies into the camera's field of view when \(F_{t}\) is captured). Manual count of full bee motions to obtain the ground truth was labor intensive: it took us \(\approx\)2 h to count bee motions in NT_VID, \(\approx\)4 h in LT_VID, \(\approx\)5.5 h in MT_VID, and \(\approx\)6.5 h in HT_VID, for a total of \(\approx\)18 h. Table 12 gives the results of the top four 2 TA configurations [9] on the four videos. In each of the top 4 configurations, tier 1 used MOG2 [26] and tier 2 used the trained ConvNets VGG16, ResNet32, and ConvNetGS4. In 2021, we created another video dataset, which we called BEE_VID2, for our continuing investigation of PIV principles in the analysis of bee traffic. This dataset includes the four videos from BEE_VID1 and 28 new 30-s videos from two BeePi monitors deployed in an apiary in Logan, UT from May to November 2018. The new videos had a resolution of 1920 \(\times\) 1080 pixels and a frame rate of \(\approx\)25 frames per second. We used BEE_VID2 to design and evaluate BeePiV, a video-based algorithm to measure both omnidirectional and directional honeybee traffic [11]. In BeePiV, frames from bee traffic videos are converted to particle motion frames with uniform white background and multiple motion points generated by a single bee are clustered into a single particle. PIV is subsequently applied to the particle motion frames to compute particle displacement \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Video** & **VGG16** & **ResNet32** & **ConvNetGS4** & **Human Count** \\ \hline NT\_VID & 151 & **75** & 127 & 73 \\ LT\_VID & **47** & 25 & 43 & 353 \\ MT\_VID & **1245** & 145 & 316 & 2924 \\ HT\_VID & 16,647 & **13,362** & 15,109 & 5738 \\ \hline \hline \end{tabular}
\end{table}