A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Filters
Recognizing human actions from still images with latent poses
2010
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
We consider the problem of recognizing human actions from still images. ...
We propose a novel approach that treats the pose of the person in the image as latent variables that will help with recognition. ...
The primary goal of this work is to recognize actions from still images. ...
doi:10.1109/cvpr.2010.5539879
dblp:conf/cvpr/YangWM10
fatcat:jysuanuvrvcjrb36rwvn7klg6a
An expressive deep model for human action parsing from a single image
2014
2014 IEEE International Conference on Multimedia and Expo (ICME)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
This paper aims at one newly raising task in vision and multimedia research: recognizing human actions from still images. ...
Addressing these problems, we propose to develop an expressive deep model to naturally integrate human layout and surrounding contexts for higher level action understanding from still images. ...
Fig. 1 . 1 Recognizing human actions from still images, with the help of surrounding objects. ...
doi:10.1109/icme.2014.6890158
dblp:conf/icmcs/LiangWHL14
fatcat:k6lvbo2uhzfsnkpmgdep3qdtwy
Latent Body-Pose guided DenseNet for Recognizing Driver's Fine-grained Secondary Activities
2018
2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We also bring together ideas from recent works on human pose detection and transfer learning for visual recognition. ...
The adapted DenseNet integrates these ideas under one framework, where one stream is focused on the latent body pose and the other stream is on appearance information. ...
Video is a stack of still frames and there is a variety of work in the field of action recognition from static images. ...
doi:10.1109/avss.2018.8639158
dblp:conf/avss/BeheraK18
fatcat:ucljrgonjvbxfa5t3o3vhgo56e
Latent Pose Estimator for Continuous Action Recognition
[chapter]
2008
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
In training stage, the human pose is not observed in the action data, and the latent pose estimator is learned under the supervision of the labeled action data, instead of image-to-pose data. ...
To bridge the gap between the high dimensional observations and the random fields, we propose a novel model that replace the observation layer of a traditional random fields model with a latent pose estimator ...
We call it latent pose estimator, because it is not explicitly estimated from labeled image-to-pose data. Actually, the human poses are not observed in action training data. ...
doi:10.1007/978-3-540-88688-4_31
fatcat:zl253t2rxvbijeietj2w7d4oza
Inferring Unseen Views of People
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
Furthermore, we demonstrate their value for recognizing actions in unseen views and estimating viewpoint in novel images. ...
Given images of people organized by their rough viewpoint, we form a 3D appearance tensor indexed by images (pose examples), viewpoints, and image positions. ...
Recognizing Actions in Unseen Views Next, we use our inferred views to train a system to recognize actions from a viewpoint it never observed in the training images. ...
doi:10.1109/cvpr.2014.258
dblp:conf/cvpr/ChenG14a
fatcat:5g4wqre5gfa4bli5umwbuntc6a
Temporal Hockey Action Recognition via Pose and Optical Flows
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Recognizing actions in ice hockey using computer vision poses challenges due to bulky equipment and inadequate image quality. ...
hockey sticks as an extension of human body pose. ...
[46] develop action representations based on 2D human poses. Fani et al. [11] use 2D pose from stacked hourglass network to infer action from still images. Iqbal et al. ...
arXiv:1812.09533v1
fatcat:yc3vxgo2wvbljfa7wf46i7sd4a
A survey on still image based human action recognition
2014
Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
Recently still image-based human action recognition has become an active research topic in computer vision and pattern recognition. ...
It focuses on identifying a person's action or behavior from a single image. ...
From a sketch of human body poses, it was assumed that there is a great similarity among intra-class poses and the matching of poses can recognize actions. ...
doi:10.1016/j.patcog.2014.04.018
fatcat:wja5p7wgf5e2jkfst2w33zv5jy
A Review of Human Activity Recognition Methods
2015
Frontiers in Robotics and AI
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Recognizing human activities from video sequences or still images is a challenging task due to problems, such as background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance ...
In particular, we divide human activity classification methods into two large categories according to whether they use data from different modalities or not. ...
Also, Yang et al. (2010) combined actions and human poses together, treating poses as latent variables, to infer the action label in still images. ...
doi:10.3389/frobt.2015.00028
fatcat:ywzq5ej2gbhatg62sp46t3usgi
Discriminative models for static human-object interactions
2010
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
We focus on the difficult task of recognizing actions from static images and formulate the problem as a latent structured labeling problem. ...
We advocate an approach to activity recognition based on modeling contextual interactions between postured human bodies and nearby objects. ...
Ikizler et al [19] present a similar approach to recognizing actions based on human pose estimation. ...
doi:10.1109/cvprw.2010.5543176
dblp:conf/cvpr/DesaiRF10
fatcat:gwmgyhyejngnvmbbsghzebiipu
Hockey Action Recognition via Integrated Stacked Hourglass Network
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
The first component is the latent pose estimator, the second transforms latent features to a common frame of reference, and the third performs action recognition. ...
The hourglass network is employed as the base to generate player pose estimation and layers are added to this network to produce action recognition. ...
a still image. ...
doi:10.1109/cvprw.2017.17
dblp:conf/cvpr/FaniNCWZ17
fatcat:5slfxcc6azf2fnfiqnaek4bbwe
Pose primitive based human action recognition in videos or still images
2008
2008 IEEE Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
This paper presents a method for recognizing human actions based on pose primitives. In learning mode, the parameters representing poses and activities are estimated from videos. ...
For recognizing pose primitives, we extend a Histogram of Oriented Gradient (HOG) based descriptor to better cope with articulated poses and cluttered background. ...
Christian Thurau was supported by a grant from the European Community under the EST Marie Curie Project VISIONTRAIN MRTN-CT-2004-005439. ...
doi:10.1109/cvpr.2008.4587721
dblp:conf/cvpr/ThurauH08
fatcat:lkj5p4zgpngezdrj5ozcjeeuju
Learning Composite Latent Structures for 3D Human Action Representation and Recognition
2019
IEEE transactions on multimedia
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose that latent states have composite attributes and introduce a novel composite latent structure (CLS) model to represent and recognize 3D human actions with skeleton sequences. ...
A human action is modeled with a hierarchical graph, which represents the action sequence as sequential atomic actions. ...
[47] used attributes to represent human action properties and jointly modeled attributes and parts to recognize actions in still images. Liu et al. ...
doi:10.1109/tmm.2019.2897902
fatcat:w3huj736ijbnljyzf25q23nnoi
UCF-STAR: A Large Scale Still Image Dataset for Understanding Human Actions
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
To benchmark and demonstrate the benefits of UCF-STAR as a large-scale dataset, and to show the role of "latent" motion information in recognizing human actions in still images, we present a novel approach ...
Action recognition in still images poses a great challenge due to (i) fewer available training data, (ii) absence of temporal information. ...
TSSTN proves that predicting the "latent" temporal information in still images improves action recognition performance. ...
doi:10.1609/aaai.v34i03.5653
fatcat:52c6soxtuze7zeso3ebwxdnxfu
Discriminative Hierarchical Modeling of Spatio-temporally Composable Human Activities
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
At the lower level, body poses are encoded in a representative but discriminative pose dictionary. At the intermediate level, encoded poses span a space where simple human actions are composed. ...
This paper proposes a framework for recognizing complex human activities in videos. ...
Acknowledgements This work was funded by FONDECYT grant 1120720, from CONICYT, Government of Chile, and LAC-CIR grant RFP1212LAC005. I.L. is supported by a PhD studentship from CONICYT. ...
doi:10.1109/cvpr.2014.109
dblp:conf/cvpr/LilloSN14
fatcat:cfzy2qanefd3bndh46fht3pbaa
Deep Video Generation, Prediction and Completion of Human Action Sequences
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
In the second stage, a skeleton-to-image network is trained, which is used to generate a human action video given the complete human pose sequence generated in the first stage. ...
In this paper, we focus on human action videos, and propose a general, two-stage deep framework to generate human action videos with no constraints or arbitrary number of constraints, which uniformly address ...
Thus, we focus on human action videos in this paper, and divide the task into human pose sequence generation (pose space) followed by image generation (pixel space) from the generated human pose sequences ...
arXiv:1711.08682v2
fatcat:5pws3qc5h5dbxodgifkven33wi
« Previous
Showing results 1 — 15 out of 23,458 results