24 datasets found

The Penn Action dataset is a real video dataset of people performing various indoor and outdoor sports with annotations of human joint locations.

Actions as space-time shapes

UCF Sports dataset consists of 150 videos from sport broadcasts covering 10 action categories.

A real-world skeleton-based human action recognition dataset.

A real-world skeleton-based human action recognition dataset.

The HMDB dataset consists of 6766 videos from 51 different action categories. The videos are generally of low quality, with strong camera motion, and non-centered people.

A neuromorphic vision sensing (NVS) device represents visual information as sequences of asynchronous discrete events (a.k.a., “spikes”) in response to changes in scene...

A neuromorphic vision sensing (NVS) device represents visual information as sequences of asynchronous discrete events (a.k.a., “spikes”) in response to changes in scene...

The HMDB51 dataset is a video dataset for human action recognition. It contains 6,767 videos annotated with 51 categories of human actions.

The Kinetics-700 dataset is a large-scale video dataset for human action recognition. It contains 555,774 videos annotated with 700 categories of human actions.

The dataset used in the paper for self-supervised learning of video representations.

The KTH dataset consists of videos of 25 people performing different activities.

UCF-101 dataset is a large-scale action recognition dataset, containing 13,320 videos categorized into 101 human action categories.

A video dataset for action recognition, consisting of 920 videos of 21 different actions.

The Kinetics-600 dataset consists of 392k training videos and 30k validation videos in 600 human action categories.

The Kinetics dataset is a large-scale video dataset for human action recognition.

Motion has shown to be useful for video understanding, where motion is typically represented by optical flow. However, computing flow from video frames is very time-consuming....

Motion has shown to be useful for video understanding, where motion is typically represented by optical flow. However, computing flow from video frames is very time-consuming....

The UCF101 dataset contains 13320 videos distributed in 101 action categories. This dataset is different from the above ones in that it contains mostly coarse sports activities...

Video classification is a fundamental problem in many video-based tasks. Applications such as autonomous driving technology, controlling drones and robots are driving the demand...