1,308 research outputs found
When Kernel Methods meet Feature Learning: Log-Covariance Network for Action Recognition from Skeletal Data
Human action recognition from skeletal data is a hot research topic and
important in many open domain applications of computer vision, thanks to
recently introduced 3D sensors. In the literature, naive methods simply
transfer off-the-shelf techniques from video to the skeletal representation.
However, the current state-of-the-art is contended between to different
paradigms: kernel-based methods and feature learning with (recurrent) neural
networks. Both approaches show strong performances, yet they exhibit heavy, but
complementary, drawbacks. Motivated by this fact, our work aims at combining
together the best of the two paradigms, by proposing an approach where a
shallow network is fed with a covariance representation. Our intuition is that,
as long as the dynamics is effectively modeled, there is no need for the
classification network to be deep nor recurrent in order to score favorably. We
validate this hypothesis in a broad experimental analysis over 6 publicly
available datasets.Comment: 2017 IEEE Computer Vision and Pattern Recognition (CVPR) Workshop
RGBD Datasets: Past, Present and Future
Since the launch of the Microsoft Kinect, scores of RGBD datasets have been
released. These have propelled advances in areas from reconstruction to gesture
recognition. In this paper we explore the field, reviewing datasets across
eight categories: semantics, object pose estimation, camera tracking, scene
reconstruction, object tracking, human actions, faces and identification. By
extracting relevant information in each category we help researchers to find
appropriate data for their needs, and we consider which datasets have succeeded
in driving computer vision forward and why.
Finally, we examine the future of RGBD datasets. We identify key areas which
are currently underexplored, and suggest that future directions may include
synthetic data and dense reconstructions of static and dynamic scenes.Comment: 8 pages excluding references (CVPR style
A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset
This paper aims to determine which is the best human action recognition
method based on features extracted from RGB-D devices, such as the Microsoft
Kinect. A review of all the papers that make reference to MSR Action3D, the
most used dataset that includes depth information acquired from a RGB-D device,
has been performed. We found that the validation method used by each work
differs from the others. So, a direct comparison among works cannot be made.
However, almost all the works present their results comparing them without
taking into account this issue. Therefore, we present different rankings
according to the methodology used for the validation in orden to clarify the
existing confusion.Comment: 16 pages and 7 table
Recent advances in video-based human action recognition using deep learning: A review
© 2017 IEEE. Video-based human action recognition has become one of the most popular research areas in the field of computer vision and pattern recognition in recent years. It has a wide variety of applications such as surveillance, robotics, health care, video searching and human-computer interaction. There are many challenges involved in human action recognition in videos, such as cluttered backgrounds, occlusions, viewpoint variation, execution rate, and camera motion. A large number of techniques have been proposed to address the challenges over the decades. Three different types of datasets namely, single viewpoint, multiple viewpoint and RGB-depth videos, are used for research. This paper presents a review of various state-of-the-art deep learning-based techniques proposed for human action recognition on the three types of datasets. In light of the growing popularity and the recent developments in video-based human action recognition, this review imparts details of current trends and potential directions for future work to assist researchers
- …