Optical Flow Estimation is the problem of finding pixel-wise motions between consecutive images.
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The objective of this paper is self-supervised learning from video, in particular for representations for action recognition.
The problem of tracking self-motion as well as motion of objects in the scene using information from a camera is known as multi-body visual odometry and is a challenging task.
Recently, the development of deep learning based methods has inspired new approaches to tackle the PIV problem.
The keys to success lie in the use of cost volume and coarse-to-fine flow inference.
The system uses a low-light video feed processed in real-time by an optical-flow network, spatial and temporal networks, and a Support Vector Machine to identify shootings, assaults, and thefts.
An object's geocentric pose, defined as the height above ground and orientation with respect to gravity, is a powerful representation of real-world structure for object detection, segmentation, and localization tasks using RGBD images.
Special cameras that provide useful features for face anti-spoofing are desirable, but not always an option.
In this paper, we propose a fast but effective way to extract motion features from videos utilizing residual frames as the input data in 3D ConvNets.
Video super-resolution (VSR) aims to restore a photo-realistic high-resolution (HR) video frame from both its corresponding low-resolution (LR) frame (reference frame) and multiple neighboring frames (supporting frames).