## 5 Discussion
Our experiments suggest that a more principled automation of RF designs for specific image datasets may yield classifiers whose performance approaches the performance of hand- or auto-designed ConvNets. An advantage of RFs for EBM projects or for other low budget research projects and citizen science initiatives is that their training and validation is much faster than the training and validation of ConvNets or SVMs and does not require continuous access to large GPU farms. For example, it took us a total of 917.52 GPU hours (38.23 days) to finish training, testing, and validating all ConvNets with greedy GS and another 360.25 GPU hours (15 days) to train, test, and validate all hand-designed ConvNets, ResNet32, and VGG16. By way of comparison, training, testing, and validating all RFs on the same GPU computer took only \(\approx\)15 h.  
Another observation we made is that our hand- and auto-designed ConvNets appear to have performed mostly on par with ResNet32 or VGG16, which suggests that all training and validation of DL models is local. ConvNet architectures that do well on large generic datasets are not guaranteed to do well on more specialized datasets. The overall performance of all classifiers on BEE1 was better than their overall performance on BEE2. A visual comparison of BEE1 and BEE2 datasets shows that BEE1 appears to be more homogenous in that the images in the training, testing, and validation datasets, while statistically significantly different in terms of contrast, energy, and homogeneity, appear to be more qualitatively similar to each other than the images in the training, testing, and validation datasets of BEE2. Another qualitative difference between BEE1 and BEE2 is that the validation dataset of BEE2 contains many more images of bee shadows than the validation dataset of BEE1. The image size of BEE1 (32 \(\times\) 32) is smaller than the image size of BEE2 (64 \(\times\) 64), which indicates that ConvNets, random forests, and SVMs may generalize better on smaller images than on larger ones. Our evaluation of the proposed method on bee traffic videos is only preliminary. Hence, our observations should be interpreted with caution. Nonetheless, they provide valuable insights into how bee motion counts can be used in estimating omnidirectional bee traffic levels. These insights will inform our future work on video analysis of bee traffic. In some cases, the proposed method overestimated the human bee motion counts. Our analysis of the performance videos (see the Supplementary Materials) showed that a major cause of overestimation was overlapping motion regions. When several motion points were detected around a single bee, the motion regions cropped around them included the same bee whose motion was counted several times. This limitation can be met by obtaining estimates of overlap and counting motion regions with significant overlap as one motion region. Another cause of overestimation was false positives when the second tier of the proposed bee motion counting method misclassified motion regions containing bee shadows, grass blades or leaves as bees. One way to address false positives is to make training and testing datasets more representative by adding to them more samples of bee shadows, grass blades, and leaves. In some cases, the proposed method underestimated the human bee motion counts. This underestimation appears to have been caused by false negatives, when second tier classifiers failed to recognize bees, as well as motion detection failures when the first tier motion detectors failed to detect motion altogether. The latter was observed with fast flying bees. We plan to experiment with higher video resolutions to estimate whether they can help us capture fast motions more accurately.