HAVIC MED Progress Test -- Videos, Metadata and Annotation

Item Name: HAVIC MED Progress Test -- Videos, Metadata and Annotation
Author(s): Amanda Morris, Stephanie Strassel, Xuansong Li, Brian Antonishek, Jonathan G. Fiscus
LDC Catalog No.: LDC2019V01
ISBN: 1-58563-882-X
ISLRN: 536-525-931-033-8
DOI: https://doi.org/10.35111/fnzz-kn07
Release Date: April 15, 2019
Member Year(s): 2019
DCMI Type(s): MovingImage, Text
Data Source(s): web collection
Project(s): HAVIC
Application(s): event detection
Language(s): English
Language ID(s): eng
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2019V01 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Morris, Amanda, et al. HAVIC MED Progress Test -- Videos, Metadata and Annotation LDC2019V01. Hard Drive. Philadelphia: Linguistic Data Consortium, 2019.
Related Works: View


HAVIC MED Progress Test -- Videos, Metadata and Annotation was developed by the Linguistic Data Consortium (LDC) and is comprised of approximately 3,650 hours of user-generated videos with annotation and metadata.

To advance multimodal event detection and related technologies, LDC developed, in collaboration with NIST (the National Institute of Standards and Technology), a large, heterogeneous, annotated multimodal corpus for HAVIC (the Heterogeneous Audio Visual Internet Collection) that was used in the NIST-sponsored MED (Multimedia Event Detection) task for several years. HAVIC MED Progress Test is a subset of that corpus, specifically, a collection of event and background videos for the HAVIC project originally released to support the 2012, 2013, 2014, and 2015 Multimedia Event Detection tasks.


The data consists of videos of various events (event videos) and videos completely unrelated to events (background videos) harvested by a large team of human annotators. Each event video was manually annotated with a set of judgments describing its event properties and other salient features. Background videos were labeled with topic and genre categories.

All video files are in .mp4 format (h.264), with varying bit-rates and levels of audio fidelity and video resolution. Metadata and annotation for the videos are stored in a .tsv file.


Please view this video sample and annotation sample


None at this time.

Additional Licensing Instructions

This members-only corpus is available to current members. Contact ldc@ldc.upenn.edu for information about becoming a member.

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