HAVIC MED Novel 1 Test -- Videos, Metadata and Annotation
|Item Name:||HAVIC MED Novel 1 Test -- Videos, Metadata and Annotation|
|Author(s):||Xuansong Li, Stephanie Strassel, Karen Jones, Brian Antonishek, Jonathan G. Fiscus|
|LDC Catalog No.:||LDC2022V01|
|Release Date:||March 15, 2022|
|DCMI Type(s):||MovingImage, Text|
|Data Source(s):||web collection|
|Online Documentation:||LDC2022V01 Documents|
|Licensing Instructions:||Subscription & Standard Members, and Non-Members|
|Citation:||Li, Xuansong, et al. HAVIC MED Novel 1 Test -- Videos, Metadata and Annotation LDC2022V01. Web Download. Philadelphia: Linguistic Data Consortium, 2022.|
HAVIC MED Novel 1 Test -- Videos, Metadata and Annotation was developed by the Linguistic Data Consortium (LDC) and is comprised of approximately 3,800 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 Novel 1 Test is a subset of that corpus, specifically, a collection of videos, metadata and annotation for the HAVIC project originally released to support the 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 (MP4).
None at this time.
Additional Licensing Instructions
This 'members-only' corpus is available to current members. Contact email@example.com for information about becoming a member.