Technical Report of the DAISY System -- Shooter Localization, Models, Interface, and Beyond
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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 to organize 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.
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