  Beyond traditional security methods, unmanned aerial vehicles (UAVs) have
become an important surveillance tool used in security domains to collect the
required annotated data. However, collecting annotated data from videos taken
by UAVs efficiently, and using these data to build datasets that can be used
for learning payoffs or adversary behaviors in game-theoretic approaches and
security applications, is an under-explored research question. This paper
presents VIOLA, a novel labeling application that includes (i) a workload
distribution framework to efficiently gather human labels from videos in a
secured manner; (ii) a software interface with features designed for labeling
videos taken by UAVs in the domain of wildlife security. We also present the
evolution of VIOLA and analyze how the changes made in the development process
relate to the efficiency of labeling, including when seemingly obvious
improvements did not lead to increased efficiency. VIOLA enables collecting
massive amounts of data with detailed information from challenging security
videos such as those collected aboard UAVs for wildlife security. VIOLA will
lead to the development of new approaches that integrate deep learning for
real-time detection and response.
