The Kinetics dataset is a large-scale, high-quality dataset for human action recognition in videos. The dataset consists of around 500,000 video clips covering 600 human action classes with at least 600 video clips for each action class. Each video clip lasts around 10 seconds and is labeled with a single action class. The videos are collected from YouTube.
1,199 PAPERS • 28 BENCHMARKS
AVA is a project that provides audiovisual annotations of video for improving our understanding of human activity. Each of the video clips has been exhaustively annotated by human annotators, and together they represent a rich variety of scenes, recording conditions, and expressions of human activity. There are annotations for:
98 PAPERS • 7 BENCHMARKS
Spatio-temporal action detection is an important and challenging problem in video understanding. The existing action detection benchmarks are limited in aspects of small numbers of instances in a trimmed video or low-level atomic actions. This paper aims to present a new multi-person dataset of spatio-temporal localized sports actions, coined as MultiSports. We first analyze the important ingredients of constructing a realistic and challenging dataset for spatio-temporal action detection by proposing three criteria: (1) multi-person scenes and motion dependent identification, (2) with well-defined boundaries, (3) relatively fine-grained classes of high complexity. Based on these guidelines, we build the dataset of MultiSports v1.0 by selecting 4 sports classes, collecting 3200 video clips, and annotating 37701 action instances with 902k bounding boxes. Our dataset is characterized with important properties of high diversity, dense annotation, and high quality. Our MultiSports, with its
13 PAPERS • 1 BENCHMARK
VidHOI is a video-based human-object interaction detection benchmark. VidHOI is based on VidOR which is densely annotated with all humans and predefined objects showing up in each frame. VidOR is also more challenging as the videos are non-volunteering user-generated and thus jittery at times.
6 PAPERS • 2 BENCHMARKS
JRDB-Act is an extension of the JRDB dataset to create a large-scale multi-modal dataset for spatio-temporal action, social group and activity detection.
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The LIRIS human activities dataset contains (gray/rgb/depth) videos showing people performing various activities taken from daily life (discussing, telphone calls, giving an item etc.). The dataset is fully annotated, where the annotation not only contains information on the action class but also its spatial and temporal positions in the video. It was originally shot for the ICPR-HARL 2012 competition.
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