## 4 Experiments  
### Omnidirectional Bee Traffic  
In our first experiment, we compared the performance of the proposed DPIV-based bee motion estimation algorithm with our previously proposed two-tier method of omnidirectional bee motion counting based on motion detection and motion region classification to estimate levels of omnidirectional bee traffic [6]. In that earlier investigation, we also made a preliminary, proof-of-concept evaluation of the two-tier method's performance on four randomly selected bee traffic videos. Specifically, we took approximately 3500 timestamped 30-s bee traffic videos captured by two deployed BeePi monitors in June and July 2018. From this collection, we took a random sample of 30 videos from the early morning (06:00-08:00), a random sample of 30 videos from the early afternoon (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 (19:00-21:00). Each video consisted of 744 \(640\times 480\) frames. Then, we randomly selected one video from the 30 early morning videos, one from the 30 early afternoon videos, one from the 30 late afternoon videos, and one from the 30 evening videos. We chose these time periods to ensure that we had videos with different traffic levels. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Img. Size & \(160\times 120,320\times 240,640\times 480\) \\ \hline Inter. Win. Size & \(n\times n\), where \(n\in\{6,8,12,16,20,32,64,90\}\) \\ \hline Inter. Win. Overlap (\%) & \(10,30,50\) \\ \hline Inter. Win. Correlation & FFT \\ \hline Time Delta (DT = 1/FPS) & 0.04 (25 frames per second) \\ \hline Signal to Noise Ratio & peak1/peak2 with a threshold of 0.05 \\ \hline Spurious Vector Replacement & local mean with kernel size of 2 and max. iter. limit of 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: DPIV parameters used in the proposed bee motion estimation algorithm. Figure 4: Degree ranges used to classify DPIV vectors as lateral, incoming, and outgoing. For each of the four videos, we manually 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. In addition to counting motions of bees present in each pair of consecutive frames \(F_{t}\) and \(F_{t+1}\), we also counted as full bee motions when bees appeared in the subsequent frame \(F_{t+1}\) but were not present in the previous frame \(F_{t}\) (e.g., when a bee flew into the camera's field of view when \(F_{t+1}\) was captured). We classified the four videos on the basis of our 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 \(\approx\)18 h for all four videos. Table 2 is reproduced from our article [6], where we compared the performance of our four best configurations of the two-tier method for omnidirectional bee counting: MOG2/VGG16, MOG2/ResNet32, MOG2/ConvNetGS3, and MOG2/ConvNetGS4. The first element in each configuration (e.g., MOG2 [22]) specifies a motion detection algorithm; the second element (e.g., VGG16 [23]) specifies a classifier that classifies each motion region detected by the algorithm specified in the first element. We compared the omnidirectional bee motion counts in Table 2 with the omnidirectional bee motion estimates returned by the DPIV-based bee motion estimation algorithm (i.e., \(T_{v}\) values in Equation (4)). Since the configuration MOG2/VGG16 gave us the overall best results in our previous investigation [6], we compared the proposed algorithm's estimates with the outputs of this configuration (see the VGG16 column in Table 2).