Sensorless Pose Determination using Randomized Action Sequences
نویسندگان
چکیده
منابع مشابه
View invariant identification of pose sequences for action recognition
In this paper, we represent human actions as short sequences of atomic body poses. The knowledge of body pose is stored only implicitly as a set of silhouettes seen from multiple viewpoints; no explicit 3D poses or body models are used, and individual body parts are not identified. Actions and their constituent atomic poses are extracted from a set of multiview multiperson video sequences by an...
متن کاملHead Pose Determination Using Synthetic Images
In this paper, we propose a new approach to determine the head pose which is a very important issue in several new applications. Our method consists of building a synthetic image database for a dense set of pose parameter values. This can be done with only one real image of the face using the Candide-3 model. To determine the pose, we compare each synthesized face image to the current image usi...
متن کاملPose and Illumination Invariant Face Recognition Using Video Sequences
Pose and illumination variations remain a persistent challenge in face recognition. In this paper, we present a framework for face recognition from video sequences that is robust to large changes in facial pose and lighting conditions. Our method is based on a recently obtained theoretical result that can integrate the effects of motion, lighting and shape in generating an image using a perspec...
متن کاملPose determination and plane measurement using a trapezium
In this paper, a new affine invariant of trapezia is introduced, and the projection of trapezia is deduced from this invariant. Known the lengths of the two parallel sides of a trapezium, pose estimation and plane measurement can be realized in a very simple way from the projection of the trapezium. Experiments on simulated and real images show that the approach is robust and accurate. Two para...
متن کاملHuman action recognition using Pose-based discriminant embedding
Manifold learning is an efficient approach for recognizing human actions. Most of the previous embedding methods are learned based on the distances between frames as data points. Thus they may be efficient in the frame recognition framework, but they will not guarantee to give optimum results when sequences are to be classified as in the case of action recognition in which temporal constraints ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Entropy
سال: 2019
ISSN: 1099-4300
DOI: 10.3390/e21020154