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Beyond traditional security methods, unmanned aerial vehicles (UAVs) havebecome an important surveillance tool used in security domains to collect therequired annotated data. However, collecting annotated data from videos takenby UAVs efficiently, and using these data to build datasets that can be usedfor learning payoffs or adversary behaviors in game-theoretic approaches andsecurity applications, is an under-explored research question. This paperpresents VIOLA, a novel labeling application that includes (i) a workloaddistribution framework to efficiently gather human labels from videos in asecured manner; (ii) a software interface with features designed for labelingvideos taken by UAVs in the domain of wildlife security. We also present theevolution of VIOLA and analyze how the changes made in the development processrelate to the efficiency of labeling, including when seemingly obviousimprovements did not lead to increased efficiency. VIOLA enables collectingmassive amounts of data with detailed information from challenging securityvideos such as those collected aboard UAVs for wildlife security. VIOLA willlead to the development of new approaches that integrate deep learning forreal-time detection and response.

Video Labeling for Automatic Video Surveillance in Security Domains