## 5 Conclusions  
Our experiments indicate that the proposed DPIV-based bee motion estimation algorithm performs on par with the two-tier bee motion counting algorithm proposed in our previous research [6] in terms of measuring omnidirectional honeybee traffic. However, unlike the two-tier algorithm that can estimate only omnidirectional honeybee traffic, the proposed algorithm can measure both omnidirectional and directional honeybee traffic. The proposed algorithm shows that DPIV can be used not only for quantifying particle velocities but also for analyzing instantaneous and longitudinal flow structures. Another advantage of the current algorithm is that it does not rely on image classification and, consequently, can be used to measure traffic of other insects such as bumble bees and ants. Insomuch as DPIV is a purely analytical technique, it requires no machine learning and no large curated datasets. By contrast, our two-tier algorithm uses machine learning methods (deep neural networks and random forests) that took us 1292.77 GPU h (53.87 days) to train, test, and validate on our curated datasets of 167,261 honeybee images [6; 28; 29; 30]. The network of six raspberry pi computers can process all captured videos within every 24-h data capturing period. The fact that the proposed DPIV-based bee motion estimation algorithm provides us directionality and insect-independent motion measurement is an acceptable price to pay for its relative slowness. As the raspberry pi hardware improves, we expect this processing time to decrease. A fundamental limitation of DPIV is its dynamic velocity range (DVR) [31], which is the ratio of the maximum measurable velocity and the minimum measurable velocity. The maximum measurable velocity is the maximum allowable particle displacement within each interrogation area on the paired images (see Figure 2). The minimum measurable velocity is the smallest measurable particle displacement between pixels in the paired images. To overcome the DPIV limitation, traditional DPIV research has required significant capital and maintenance investments, which confined DPIV applications mostly to academic or industrial research environments [32]. By distributing the algorithm's computation over six raspberry pi computers, we showed that useful DPIV-based results can be obtained on a hardware base that costs less than 1000 USD. This result bodes well for citizen science in that the hardware base can be replicated without significant capital and maintenance costs. We are also currently investigating several techniques of combining multiple neighboring vectors into a single bee. While our preliminary experiments indicate that such techniques may work on low-traffic and medium-traffic videos, they may have issues with high-traffic videos. We have learned from this investigation that obtaining ground truth videos to estimate the accuracy of bee motion counts is very labor intensive and time consuming. It took us \(\approx\)18 h to obtain human bee motion counts on four videos of live honeybee traffic. We are currently working on curating more live honeybee traffic videos and hope to make them public in our future publications. One curious observation we have made about lateral traffic is that the orientation of the bees engaged in it typically remains perpendicular to the hive (i.e., the bees' heads face the hive). Another curious observation we have made about lateral traffic is that sometimes a bee engages in lateral traffic before entering the hive and sometimes, after doing several sidewise maneuvers, she leaves the vicinity of the hive captured by the camera. The laterally moving bees that eventually enter the hive may use lateral moves to better home in on the entry point, whereas the laterally moving bees that fly away might be stray bees looking for a new home or scouts from other hives checking if the hive is appropriate for robbing. As computer scientists, however, we have no explanation of these observations and leave them for entomologists to investigate.