# Semi-Automatic Cloud-Native Video Annotation for Autonomous Driving
## 7 Conclusions  
An innovative solution named Annotation as a Service (AaaS) has been designed specifically to integrate heterogeneous video annotation workflows into containers and take advantage of a highly scalable and reliable design based on Kubernetes workloads. The AaaS was systematically tested in Kubernetes clusters at different scales of computing resources. It scaled efficiently in large situations. Specifically it was tested in an experiment with 25 TB of input data to be annotated, 4000 concurrent annotation jobs, and a HPC-cluster of 32 worker nodes with a total of 512 cores and 2048 GB of RAM. This experiment demonstrated that the AaaS is a robust and resilient solution. On the cost side, the increasing volumes of workloads showed a reduction in the effective cost per task in the scaled-up solutions. A bottleneck for any annotation solution with the scale of data that we have discussed, is in the available bandwidth for the transmission of large amounts of data to the cloud and its cost. A hybrid edge-cloud and multi-zone cloud solution is presented as the best design option for an ADAS data annotation tool so that data is logged, at least in the initial automatic stages, close to where it is created. The heterogeneity and agnosticism of the proposed AaaS make it suitable to be deployed in  
Figure 9: Manual annotation tool for driver action recognition (DMD (Driver Monitoring Dataset) dataset), which loads pre-annotation and allows human operators to review/modify them. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Human Annotation Time [min]**} \\  & & & \multicolumn{4}{c}{**(Annotation-Clip Ratio)**} \\ \hline \multirow{2}{*}{**Annotation Task**} & **Num.** & **Clip Length** & **Fully Manual** & **Semi-Automatic** & **Time Saving** \\  & **Annotators** & **[s]** & **[avg. \(\pm\) std.]** & **[avg. \(\pm\) std.]** & **[avg. (max.)]** \\ \hline Lane marking & 11 & 30 & 39:1 \(\pm\) 15:1 & 16:1 \(\pm\) 8:1 & 60\% (82\%) \\ \hline Driver action recognition & 4 & 180 & 93:1 \(\pm\) 61:1 & 34:1 \(\pm\) 23:1 & 63\% (81\%) \\ \hline Pixel-wise & \multirow{2}{*}{4} & single & \multirow{2}{*}{97:1 \(\pm\) 44:1} & \multirow{2}{*}{70:1 \(\pm\) 30:1} & \multirow{2}{*}{28\% (33\%)} \\ semantic segmentation & & frame * & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Time spent by human operators to complete different annotation tasks versus sequence time. Specifically, it is the average time obtained from various human operators who performed the same annotation task, from scratch (fully manual) and from previous annotations (semi-automatic). The number of annotators per task, the length of the video, and the time savings due to the semi-automatic approach are also represented. * Note the pixel-wise annotation task was performed on a single image rather than a video. this type of hybrid system towards a computing continuum context. The latter is considered one of the essential contributions of this research to a state of the art of annotation tools not designed to scale in this context. The main limitations to achieve the ubiquitous and hybrid deployment aforementioned for the AaaS were in the configuration and implementation of the specific services of each PaaS, as well as in the compliance with the different restrictions inherent to IaaS between cloud providers. These challenges are beyond the scope of the current study, but they are the direction of future research. Finally, the AaaS has been included in a context of semi-automatic annotation processes for AD/ADAS. Generating ground truth, i.e. with humans in the loop, is extremely expensive. With the help of previous annotations from the proposed system, the time of human participation in the annotation could be reduced up to 80% maximum and 60% on average. For reference, annotating lane markings or 3D objects from a 60-second video sequence can take 50-300 min of human labor, depending on the complexity of the scene.