Nowadays a huge number of user-generated videos are uploaded to social media every second, capturing glimpses of events all over the world . These videos provide important and useful information for reconstructing the events. In this paper , we describe the DAISY system, enabled by established machine learning techniques and physics models, that can localize the shooter location only based on a couple of user-generated videos that capture the gun shot sound . The DAISY system utilizes machine learning techniques like video synchronization and gunshot temporal localization URLanize the unstructured social media videos and quickly localize gunshot in the videos. It provides a web interface for human-in-the-loop verification to ensure accurate estimations. We present the results of estimating the shooter's location of the Las Vegas Shooting in 2017 and show that DAISY is able to get accurate location using only the first few shots . We then point out future directions that can help improve the system and further reduces human labor in the process. We publish all relevant source code  including the web interface and machine learning models in the hope that such tool can be of use to help preserve life and get contributions from the research and software engineering community to make the tool better .
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 . In this technical report , we describe a newly developed tool, the Video Event Reconstruction and Analysis (VERA) system, that enables the localization of a shooter from just a few videos that include the sound of gunshots using established machine learning techniques and straightforward physics models. This tool relies on other tools we have already developed including video synchronization and geolocation to order unstructured videos lacking metadata over time and space, and sound detection algorithms. Both this gunshot localization tool and the previous systems it incorporates are run through a web interface that enables human-in-the-loop verification to ensure accurate estimations. 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 relevant source code , including the web interface and machine learning models , are freely available in Github. We do so in the hope that this tool can be used by anyone who needs it to protect and promote human rights and public safety. We also hope that researchers and software developers will be inspired to improve and expand this system moving forward to better meet the needs of public safety and human rights .
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