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Jay D. Aronson

dblp:241/9434 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 77% Multimedia systems and quality of experience · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Multimedia systems and quality of experience › multimedia synchronization
video synchronization
0.112019
Shooter Localization Using Social Media Videos · ACM Multimedia 2019

Methods — techniques the papers use, named apart from their topics

multimodal fusion · 0.4human-in-the-loop verification · 0.4
YearPublicationVenuePosition
2019 Shooter Localization Using Videos in the Wild
abstract
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.
Junwei Liang 0001, Jay D. Aronson, Alex Hauptmann 0001
CBMI2
2019 Shooter Localization Using Social Media Videos
abstract
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 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 gunshot temporal localization to organize the unstructured social media videos 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. The full technical report, all relevant source code including the web interface and machine learning models are available.
Junwei Liang 0001, Jay D. Aronson, Alex Hauptmann 0001
ACM Multimedia2