Comparison of labeled video datasets for action recognition
Compiled by Kevin Murphy and Alex Gorban.
Last updated 29 July 2014.
What do the headers in the table mean?
- Year: year dataset was last update
- Accuracy: State of the art performance of best (whole video) classifier.
Provide a citation in the References column.
- Avg dur: Median duration of each video (s=seconds, m=minutes, h=hours).
- Tot# hours: total duration of all videos in dataset (hours).
#video level: number of types of video-level action labels.
- #frame level: number of types of frame-level action labels.
- #Act/vid: median number of (low or high level) actions per video.
- #Vid/act: median number of videos per action.
- #Ppl/vid: median number of people per video.
- Tot# Ppl: total number of people in the dataset.
- Youtube: Does it contain videos from youtube?
- Lab: Does it contain videos from controlled (laboratory) conditions?
- Sports: Does it contain videos about sports?
- Cooking: Does it contain videos about cooking?
- Movies: does it contain videos from movies or TV shows?
- Segmented: are temporal segmentations of the actions provided?
- Frame tags: are there tags at a frame level? (All videos are assumed to
labels at the whole video label.)
- NLP: are there natural language text annotations?
- Objects: are there annotations (eg. bounding boxes) for objects?
- Skeleton: is there annotation for the human skeleton pose?
- #Cameras: number of cameras uses in recording.
- Static cam: is camera stationary?
- Moving cam: is camera moving?
- Stereo: is camera stereo?
- POV: is camera point of view (head mounted)?
- RGDB: does camera record depth info?
- Resoln: image resolution
- FPS: Frames per second
- Video-level action names
- Frame-level action names (if available)
- References: references to relevant papers using keys
listed in a separate table below.
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Other relevant links
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