## 5 Discussion  
Our experiments indicate that the proposed two-tier method of coupling motion detection with image classification to count bee motions in close proximity to the landing pad of a given hive has the potential to improve estimates of omnidirectional bee traffic levels in EBM. In the proposed method, motion detection acts as a class-agnostic object location method to suggest regions with possible objects,  
Figure 12: Examples of shadow motion regions that were misclassified as BEE by all convolutional networks. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Video** & **Num. Frames** & **VGG16** & **ResNet32** & **ConvNetGS3** & **ConvNetGS4** \\ \hline NT\_vid02.mp4 & 743 & 140 & 188 & 175 & 173 \\ LT\_vid02.mp4 & 744 & 530 & 1101 & 1439 & 10,000 \\ MT\_vid02.mp4 & 743 & 1095 & 343 & 1179 & 871 \\ HT\_vid02.mp4 & 744 & 13,002 & 9716 & 9960 & 9885 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance of the system on NT_vid02.mp4, LT_vid02.mp4, MT_vid02.mp4, and HT_vid02.mp4: the columns VGG16, ResNet32, ConvNetGS3, and ConvNetGS4 give the bee motion counts returned by the configurations MOG2/VGG16, MOG2/ResNet32, MOG2/ConvNetGS3, and MOG2/ConvNetGS4, respectively. each of which is subsequently classified with a trained classier such as a ConvNet or an SVM or an ensemble of classifiers such as an RF. In our experiments, the hand-designed ConvNets performed slightly better than or on par with the auto-designed ConvNets on two of the three image datasets (BEE1 and BEE2_1S). When the hand-designed ConvNets outperformed the auto-designed ConvNets, the performance margins were not wide. Specifically, on BEE1 (see Tables 2 and 3), the best hand-designed ConvNet (ConvNet 1) and ResNet32 achieved a validation accuracy of 99.48% while the performance of the best auto-designed ConvNet (ConvNetGS-3) was 99.09%. Thus, the best auto-designed ConvNet performed almost exactly on par with the best hand-designed ConvNet and ResNet32 and outperformed the remaining seven hand-designed ConvNets, VGG16, and all RFs and SVMs. The performance margin between the best hand-designed ConvNet (ConvNet 1) and the best auto-designed ConvNet (ConvNetGS-3) was only 0.39%. On BEE2_1S (see Tables 4 and 6), the top two hand-designed ConvNets (ConvNets 1 and 3) outperformed all other models with the respective validation accuracies of 94.08% and 88.84%. The two best performing auto-designed ConvNets (ConvNetGS-4 and ConvNetGS-3) ranked fourth and fifth with the respective validation accuracies of 86.36% and 84.26%. Thus, the auto-designed ConvNets outperformed the remaining seven hand-designed ConvNets, ResNet32, and all RFs and SVMs. The performance margin between the best hand-designed ConvNet (ConvNet 1) and the best auto-designed ConvNet (ConvNetGS-4) was 7.7%. On BEE2_2S (see Tables 5 and 7), the top three places were taken by hand-designed ConvNets (ConvNets 3, 1, and 4). These ConvNets outperformed the other models with the respective validation accuracies of 78.90%, 78.17%, and 76.51%. The top auto-designed model (ConvNetGS-5) ranked fourth with a validation accuracy of 76.20% and outperformed both ResNet32 and VGG16. The performance margin between the best performing hand-designed ConvNet and the best performing auto-designed ConvNet narrowed to 2.7%. A solid performance of the RFs on BEE1 and BEE2_1S is noteworthy. Typically, RFs do well on datasets of items structured as sets of features [37]. However, in our experiments, we trained and validated the RFs on raw images without extracting any features. Each pixel position and its corresponding value can, of course, be construed as a feature-value pair, but this does not require any computationally intensive feature extraction typically done for classifiers that use feature-value pairs. While the RFs did not outperform any hand- or auto-designed ConvNets on BEE1, the validation accuracy of all RFs was above 90%. On BEE2_1S, the top performing RF with 100 decision trees performed better than four hand-designed ConvNets, ResNet32, and the best linear SVM.