Shooter Localization Using Videos in the Wild

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 in the wild provide important and useful information for reconstructing events like the Las Vegas Shooting in 2017. In this paper, we describe a system that can localize the shooter location only based on a couple of user-generated videos that capture the gunshot sound. Our system first utilizes established video analysis techniques like video synchronization and automatic gunshot processing to organize the unstructured videos in the wild for users to understand the event effectively. By combining multimodal information from visual, audio and geo-locations, our system can then visualize all possible locations of the shooter in the map. Our system 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 our system is able to get accurate location using only the first few gunshots. All relevant source code including the web interface and machine learning models are available.