  Every minute, hundreds of hours of video are uploaded to social media sites
and the Internet from around the world. This material creates a visual record
of the experiences of a significant percentage of humanity and can help
illuminate how we live in the present moment. When properly analyzed, this
video can also help analysts to reconstruct events of interest, including war
crimes, human rights violations, and terrorist acts. Machine learning and
computer vision can play a crucial role in this process. In this technical
report, we describe the Video Event Reconstruction and Analysis (VERA) system.
This new tool brings together a variety of capabilities we have developed over
the past few years (including video synchronization and geolocation to order
unstructured videos lacking metadata over time and space, and sound recognition
algorithms) to enable the reconstruction and analysis of events captured on
video. Among other uses, VERA enables the localization of a shooter from just a
few videos that include the sound of gunshots. To demonstrate the efficacy of
this suite of tools, we present the results of estimating the shooter's
location of the Las Vegas Shooting in 2017 and show that VERA accurately
predicts the shooter's location using only the first few gunshots. We then
point out future directions that can help improve the system and further reduce
unnecessary human labor in the process. All of the components of VERA run
through a web interface that enables human-in-the-loop verification to ensure
accurate estimations. All relevant source code, including the web interface and
machine learning models, is freely available on Github. We hope that
researchers and software developers will be inspired to improve and expand this
system moving forward to better meet the needs of human rights and public
safety.
