Computer Vision

We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets c

The data set has been consolidated for the task of Human Posture Recognition. The data set consists of four postures namely -

1. Sitting,
2. Standing,
3. Bending and,
4. Lying.

There are 1200 images for each of the postures listed above. The images have a dimension of 512 x 512 px.

A dataset of videos, recorded by an in-car camera, of drivers in an actual car with various facial characteristics (male and female, with and without glasses/sunglasses, different ethnicities) talking, singing, being silent, and yawning. It can be used primarily to develop and test algorithms and models for yawning detection, but also recognition and tracking of face and mouth. The videos are taken in natural and varying illumination conditions. The videos come in two sets, as described next: 

This is an eye tracking dataset of 84 computer game players who played the side-scrolling cloud game Somi. The game was streamed in the form of video from the cloud to the player. The dataset consists of 135 raw videos (YUV) at 720p and 30 fps with eye tracking data for both eyes (left and right). Male and female players were asked to play the game in front of a remote eye-tracking device. For each player, we recorded gaze points, video frames of the gameplay, and mouse and keyboard commands.