13 datasets found

The NTU-RGBD dataset is a large-scale dataset for 3D human activity analysis, containing 56,000 videos and 60 actions performed by 40 people from 80 different views.

The authors used the UCF101, HMDB51, and Diving48 datasets to evaluate the performance of their proposed algorithm.

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...

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...

The MSR-Action3D dataset has been first introduced with a depth-based approach, but its joint streams were widely used later as a reference for joint-based methods.

The dataset used for video action classification, consisting of 9.8k training videos, 1.8k validation videos, and 157 classes.

The dataset used in this paper is a Flickr image dataset, which is used to evaluate the proposed deep aesthetic feature learning framework.

The Kinetics dataset is a large-scale human action dataset, which consists of 400 action classes where each category has more than 400 videos.

The dataset used in this paper is a collection of videos for various tasks such as video recognition and action recognition.