HOHA

HOHA

Domain	Human actions
Media	Video, Annotations
Size	2.4 GB
Instances	not known
File Format	DivX 5
Creation Date	2008
Task	action recognition
Copyright	not known
URL	http://www.irisa.fr/vista/actions/

Domain

• Human Actions and Scenes
• The archive contains the following two directories:
  
  • videoclips
    
  • annotations

Comments

• HOLLYWOOD HUMAN ACTIONS (HOHA)
  This archive provides the video samples and annotations used in the experimental section of the paper "Learning realistic human actions from movies" by I. Laptev, M. Marszalek, C. Schmid and B. Rozenfeld,
  published in CVPR 2008.

Media (image, video, mixed, …)

• video samples and annotations

Size (no images, in GB, …)

• 2.4Gb

Source (FlickR, Corel)

• Videoclips sources : short sequences from 32 movies:American Beauty, As Good As It Gets, Being John Malkovich, Big Fish, The Big Lebowski, Bringing Out The Dead, The Butterfly Effect, Casablanca, The Crying Game, Dead Poets Society, Double Indemnity, Erin Brockovich, Fargo, Forrest Gump, Gandhi, The Godfather, The Graduate, I Am Sam, Independence Day, Indiana Jones And The Last Crusade, Its A Wonderful Life, Kids, LA Confidential,LOR - Fellowship Of The Ring, Lost Highway, The Lost Weekend, Mission To Mars, The Naked City, The Pianist, Pulp Fiction, Raising Arizona, Reservoir Dogs.
• The content of the annotations directory defines video samples as fragments of the video clips. The fragments are specified by frame ranges. For the automatic training, samples correspond to full clips. For manual annotations, clips could be trimmed or split. Each sample is annotated according to 8 classes: AnswerPhone, GetOutCar, HandShake, HugPerson, Kiss, SitDown, SitUp, StandUp.

Annotation type (free text, structured, …)

Ground truth

Event or project

Task (retrieval, recognition, …)

Format

• The video frames typically consist of 240 lines, the aspect ratios vary.
  The videos run at about 24 fps. The clips are encoded using the DivX 5 codec

Quality (resolution)

Creation date

• 2008

Copyright

To cite this database please use:

Ivan Laptev, Marcin Marszałek, Cordelia Schmid and Benjamin Rozenfeld, Learning Realistic Human Actions from Movies, CVPR 2008.

URL

• http://www.irisa.fr/vista/actions/