Then , building on geometric properties of the DensePose mapping , we use the video dynamic to propagate ground-truth annotations in time as well as to learn from Siamese equivariance constraints . Having performed exhaustive empirical evaluation of various data annotation and learning strategies , we demonstrate that doing so can deliver significantly improved pose estimation results over strong baselines . However , despite what is suggested by some recent works , we show that merely synthesizing motion patterns by applying geometric transformations to isolated frames is significantly less effective , and that motion cues help much more when they are extracted from videos . Algorithm performance is investigated within a numerical simulation environment reproducing realistic conditions in terms of target configuration and sensor operation . Results demonstrate that the proposed algorithms provide adequate initialization of the pose parameters as well as pose estimate in the tracking phase with 0.1 ° accuracy in attitude and sub-centimeter accuracy in relative position , operating with sparse point clouds . Finally , the effect of target visibility in the sensor field of view on the tracking algorithm performance is analyzed in detail . In addition to maintaining a sparse map of landmarks in the world , our system finds as many inter-frame point matches as possible . These point matches provide additional constraints on the inter-frame motion of the camera leading to a more accurate pose estimate , and , since they are not maintained as full map landmarks , they do not cause a large increase in the computational cost . Our results in both a simulated environment and in real video demonstrate the improvement in estimation accuracy gained by the inclusion of visual odometry style observations . In this paper , according to the existing problem of the traditional pose estimation method based on color image : manual intervention will bring the uncertainty leading to inaccurate foreground / background segmentation , and have a negative impact on the subsequent parsing process . We put forward a method which combined with the depth information captured by the Kinect , with the pre-processing of depth image , to improve pose estimation process in static images , so that the subsequent processing can be more convenient , and meanwhile the accuracy can be improved . Therefore , we extend the original voting approach of ISM and let all visual words that contribute to a person hypothesis also vote for the positions of the person 's body parts . Since this approach is not constrained to a certain feature type and different feature types can even be fused during the pose estimation process , the approach is highly flexible . We show preliminary evaluation results of our approach using on the public available HumanEva dataset which comprises ground-truth pose data and thereby provides training and evaluation data . 6 ) . In the context of action recognition the pose estimation step is often replaced by the calculation of a motion descriptor . We propose a number of different variants of our approach , including sparse encoding and ridge-regression for feature representation . While classical pose specific methods strongly depend on the quality of the pose estimation step , our approaches maintain their superior behavior even under severe pose noise . We evaluate on both BU3DFE and Multi-PIE datasets and outperform the state-of-the-art in classification accuracy , even with a simple pose specific baseline method , while being extremely robust to feature noise and erroneous viewpoint estimation with our pairwise regression approaches . The problem is caused by the identity mapping . Since Hourglass network , also called conv - deconv structure , is an effective structure for pose estimation , object detection , and pixel level tasks , we use it as the basic structure in experiments . We observe a problem of using residual unit for Hourglass : when outputs of two residual units are summed up , the output variance is approximately doubled , which causes difficulty in optimization . We present empirical tests on common in - the - wild pose benchmark datasets which show state - of - the - art results . Additionally we test our method on a dataset usually used for pose estimation using depth and start to close the gap with state - of - the - art depth pose methods . We open - source our training and testing code as well as release our pre - trained models . section : Introduction The successes of deep convolutional neural nets ( CNNs ) at image classification has spawned a flurry of work in computer vision on adapting these models to pixel - level image understanding tasks , such as boundary detection , semantic segmentation , optical flow , and pose estimation . The key ideas that have enabled this adaption thus far are : ( 1 ) deconvolution schemes that allow for upsampling coarse pooled feature maps to make detailed predictions at the spatial resolution of individual pixels , ( 2 ) skip connections and hyper - columns which concatenate representations across multi - resolution feature maps , ( 3 ) atrous convolution which allows efficient computation with large receptive fields while maintaining spatial resolution , and ( 4 ) fully convolutional operation which handles variable sized input images . The person detection counts the number of persons subsequently followed by the person tracking which determines who is stopping and who is moving . The head pose estimation denotes whether they are looking or not at the target scene . Finally the projection of the visual field extracts the location of interest in the target scene . Head pose estimation from low resolution video can be a difficult task due to the limited quantity and quality of pixels which can be harvested for information . In this work , we propose a method for tracking change in head pose by solving a texture detection problem in an orientation space . In addition , the proposed switching dynamic model gives better results than standard tracking models such as Kalman filter . While being applied to the head-pose estimation problem the methodology presented in this thesis is really general and can be used to solve various regression and tracking problems , e.g. we applied it to the tracking of a sound source in an image . Fatigue monitoring can effectively reduce or even avoid traffic accidents . Head posture estimation is one of the focuses in the field of fatigue monitoring . In this paper , according to the coordina ... Our HiBall Tracking System generates over 2000 head-pose estimates per second with less than one millisecond of latency , and less than 0.5 millimeters and 0.02 degrees of position and orientation noise , everywhere in a 4.5 by 8.5 meter room . The system is remarkably responsive and robust , enabling VR applications and experiments that previously would have been difficult or even impossible . We address the problem of automated discretization for continuous labels in the context of head pose estimation from overhead cameras . Due to the lack of visual detail , precise head pose estimates are not always possible . A common approach is to discretize the space of head pose angles , turning a real-valued prediction task into a coarser ( ordered ) classification variant . Even humans can have trouble without contextual information . In this work , we couple novel contextual information , social grouping , with two important computer vision tasks : multi-target tracking and head pose/direction estimation in surveillance video . These three components are modeled in a probabilistic formulation and we provide effective solvers . By using kernel tricks , the authors implicitly map the input data into the kernel feature space associated with the kernel function . In experiments , the proposed algorithm was applied to a head pose estimation application . Experimental results demonstrated the increased effectiveness and robustness of the method . Head pose estimation bears importance in several applications in biometrics , human-computer interfaces , driver monitoring systems , video conferencing and social interaction enhancement programs . A recent trend in head pose estimation research has been the use of manifold learning techniques to capture the underlying geometry of the images . Face images with varying pose angles can be considered to be lying on a smooth low-dimensional manifold in high-dimensional image feature space . [ Age estimation ] [ Head pose estimation ] [ Multi - label classification ] We demonstrate that a direct , holistic approach to estimating 3D head pose from image intensities using convolutional neural networks delivers superior accuracy in comparison to keypoint - based methods . While keypoint detectors have recently improved dramatically due to deep learning , head pose recovery inherently is a two step process with numerous opportunities for error . First , if sufficient keypoints fail to be detected , then pose recovery is impossible . While other works have addressed the direct regression of pose from images using conv - nets they did not include a comprehensive set of benchmarks or leverage modern deep architectures . In applications where accurate head pose estimation is required , a common solution is to utilize RGBD ( depth ) cameras . These can be very accurate , but suffer from a number of limitations : section : RELATED WORK Human head pose estimation is a widely studied task in computer vision with very diverse approaches throughout its history . In the classic literature we can discern Appearance Template Models which seek to compare test images with a set of pose exemplars . Recently , facial landmark detectors which have become very accurate , have been popular for the task of pose estimation . Also recently , work has developed on estimating head pose using neural networks . presents an in - depth study of relatively shallow networks trained using a regression loss on the AFLW dataset . We compare our results to all of these works . Chang et al . also argue for landmark - free head pose estimation . They regress 3D head pose using a simple CNN and focus on facial alignment using the predicted head pose . Analysis.- Coefficient-Tracking Speckle Reducing Anisotropic Diffusion.- Increasing Computational Redundancy of Digital Images via Multiresolutional Matching.- Efficient Removal of Noisy Borders of Monochromatic Documents.- Image Segmentation.- A Visual Perception Approach for Accurate Segmentation of Light Profiles.- Implicit Active-Contouring with MRF.- A Novel Pixon-Based Approach for Image Segmentation Using Wavelet Thresholding Method.- Interactive Image Segmentation Based on Hierarchical Graph-Cut Optimization with Generic Shape Prior.- Multiphase Segmentation Based on Implicit Active Shape Models.- Image and Video Retrieval and Indexing.- Weighted Block Matching-Based Anchor Shot Detection with Dynamic Background.- Local Triplet Pattern for Content-Based Image Retrieval.- An Interactive Image Feature Visualization System for Supporting CBIR Study.- Pattern Analysis and Recognition.- Cue Integration for Urban Area Extraction in Remote Sensing Images.- Scale Invariant Feature Transform with Irregular Orientation Histogram Binning.- Affine Invariant-Based Classification of Inliers and Outliers for Image Matching.- A New Large-Scale Multi-purpose Handwritten Farsi Database.- Memory Efficient Vision Based Line Feature Extraction for Tiny Mobile Robots.- A Swift and Memory Efficient Hough Transform for Systems with Limited Fast Memory.- Novel Framework for Selecting the Optimal Feature Vector from Large Feature Spaces.- Granular Computing and Rough Sets to Generate Fuzzy Rules.- 3D Human Pose Estimation from Static Images Using Local Features and Discriminative Learning.- Abnormal Behavior Recognition Using Self-Adaptive Hidden Markov Models.- Interacting with Digital Signage Using Hand Gestures.- Probability Error in Bayes Optimal Classifier with Intuitionistic Fuzzy Observations.- Fuzzy Gaussian Process Classification Model.- VizDraw : A Platform to Convert Online Hand-Drawn Graphics into Computer Graphics.- Biometrics.- Score Level Fusion of Ear and Face Local 3D Features for Fast and Expression-Invariant Human Recognition.- Speech-Video Synchronization Using Lips Movements and Speech Envelope Correlation.- Analyzing Human Gait Using Patterns of Translation and Rotation.- Level Set Approaches and Adaptive Asymmetrical SVMs Applied for Nonideal Iris Recognition.- A Security Analysis of Biometric Template Protection While deep learning has lead to great advances in 2D and 3D pose estimation from single video sources , the problem of estimating 3D human pose from multiple video sensors with overlapping fields of view has received less attention . When the application allows use of multiple cameras , 3D human pose estimates may be greatly improved through fusion of multi-view pose estimates and observation of limbs that are fully or partially occluded in some views . Past approaches to multi-view 3D pose estimation have used probabilistic graphical models to reason over constraints , including per-image pose estimates , temporal smoothness , and limb length . We propose a generative statistical approach to human motion modeling and tracking that utilizes probabilistic latent semantic ( PLSA ) models to describe the mapping of image features to 3D human pose estimates . PLSA has been successfully used to model the co-occurrence of dyadic data on problems such as image annotation where image features are mapped to word categories via latent variable semantics . Abstract 3D human pose estimation from a single image is a challenging problem due to occlusion , viewpoint variance , and the ill-posed nature of back projection . We follow a standard two-step pipeline which first detects 2D joint locations and uses them to infer 3D pose . Abstract 3D human pose estimation from 2D joints of an image is a worthwhile and challenging research topic . Since a specific 2D pose could be projected from various 3D poses , the ambiguity becomes a difficult obstacle when recovering 3D pose from 2D . This system has three main hybrid algorithms , namely 2D/3D human pose pre-processing algorithm , ensemble graph chaining segmented model on 2D/3D video sequence pose estimation and 3D ensemble facial expression detection algorithm . The experimental results on public benchmarks 2D/3D datasets show that our model is more efficient in solving arbitrary human pose estimation problem . Also , this model has the high true positive rate , low false detection rate compared to traditional joint human pose detection models . Abstract Automatic identification of various facial movements and expressions with high recognition value is important for human computer interaction as the facial behavior of a human can be treated as an important factor for information representation as well as communication . A high deviation of human appearance and existence of noisy contextual background makes the human pose analysis is hard to achieve . A number of basic factors such as cluttered background , occlusion , and camera movement and illumination variations degrade the image quality resulting in poor performance for identifying different facial expressions . Human pose analysis has been known to be an effective means to evaluate athlete 's performance . Marker-less 3D human pose estimation is one of the most practical methods to acquire human pose but lacks sufficient accuracy required to achieve precise performance analysis for sports . To ensure temporal consistency in the refining stage , we adopt the ranking loss for adversarial training . We conduct extensive experiments on two image-to-video translation tasks : facial expression retargeting and human pose forecasting . Superior results over the state of the art on both tasks demonstrate the effectiveness of our approach . Inspired by the recent success of deep reinforcement learning methods , in this paper we propose a new reinforcement learning formulation for the problem of human pose prediction , and develop an imitation learning algorithm for predicting future poses under this formulation through a combination of behavioral cloning and generative adversarial imitation learning . Our experiments show that our proposed method outperforms all existing state-of-the-art baseline models by large margins on the task of human pose prediction in both short-term predictions and long-term predictions , while also enjoying huge advantage in training speed . Significant progress has been made based on the development of depth sensors , i.e. , accessible human pose prediction from still depth images~\cite{rf12pami}. However , most of the existing approaches to this problem involve several components/models that are independently designed and optimized , leading to suboptimal performances . In this paper , we propose a novel inference-embedded multi-task learning framework for predicting human pose from still depth images , which is implemented with a deep architecture of neural networks . Specifically , we handle two cascaded tasks : i ) generating the heat ( confidence ) maps of body parts via a fully convolutional network ( FCN ) ; ii ) seeking the optimal configuration of body parts based on the detected body part proposals via an inference built-in MatchNet~\cite{mn15cvpr } , which measures the appearance and geometric kinematic compatibility of body parts and embodies the dynamic programming inference as an extra network layer . Predicting human pose in the wild is a challenging problem due to high flexibility of joints and possible occlusion . Existing approaches generally tackle the difficulties either by holistic prediction or multi-stage processing , which suffer from poor performance for locating challenging joints or high computational cost . Improved CNN supervision leverages first and second order parent relationships along the skeletal kinematic tree , and improved multi-level skip connections to learn better representations through implicit modification of the loss landscape . Further , transfer learning from 2D human pose prediction significantly improves accuracy and generalizability to unseen poses and camera views . Additionally , we contribute a new benchmark and training set for human body pose estimation from monocular images of real humans , that has ground truth captured with marker-less motion capture . Moreover , if the predicted future is too short ( e.g. , less than one second ) , it may not be fully usable by a human or other systems . In this paper , we propose a novel method for future human pose prediction capable of predicting multiple long-term futures . This makes the predictions more suitable for real applications . This also allows us to directly generate novel images from the trained model by sampling from the discovered latent spaces . We also demonstrate the model by prediction of human pose in an ambiguous setting . Our Bayesian framework allows us to perform disambiguation in a principled manner by including latent space priors which incorporate the dynamic nature of the data . Recently , it has been shown that similar refinement can be obtained by stacking several convolutional networks on top of each other . This led to improved results in human pose estimation and semantic instance segmentation . In this paper we adapt the idea of stacking multiple networks to optical flow estimation . Given the input shape , the goal of is to regress the shape primitive parameters which is achieved via a CNN . The method of extends classical work on heatmap regression by proposing a 4D representation for regressing the location of sparse 3D landmarks for human pose estimation . Different from , we demonstrate that a 3D volumetric representation is particular effective for learning dense 3D facial geometry . While recent advances in pose estimation have improved performance on front and side views , most real - world settings present challenging viewpoints such as top or angled views in retail stores , hospital environments , or airport settings . These viewpoints introduce high levels of self - occlusion making human pose estimation difficult for existing algorithms . Humans are remarkably robust at predicting full rigid - body and articulated poses in these challenging scenarios . Synthetic ground - truth can be established by rendering images using surface - based models . This has recently been applied to human pose in the SURREAL dataset of , where the SMPL model was rendered with the CMU Mocap dataset poses . However , covariate shift can emerge because of the different statistics of rendered and natural images . Recent pose estimation systems have universally adopted ConvNets as their main building block , largely replacing hand - crafted features and graphical models ; this strategy has yielded drastic improvements on standard benchmarks . We continue along this trajectory and introduce a novel “ stacked hourglass ” network design for predicting human pose . The network captures and consolidates information across all scales of the image . section : Conclusion We demonstrate the effectiveness of a stacked hourglass network for producing human pose estimates . The network handles a diverse and challenging set of poses with a simple mechanism for reevaluation and assessment of initial predictions .
Then , building on geometric properties of the DensePose mapping , we use the video dynamic to propagate ground-truth annotations in time as well as to learn from Siamese equivariance constraints . Having performed exhaustive empirical evaluation of various data annotation and learning strategies , we demonstrate that doing so can deliver significantly improved pose estimation results over strong baselines . However , despite what is suggested by some recent works , we show that merely synthesizing motion patterns by applying geometric transformations to isolated frames is significantly less effective , and that motion cues help much more when they are extracted from videos .
Algorithm performance is investigated within a numerical simulation environment reproducing realistic conditions in terms of target configuration and sensor operation . Results demonstrate that the proposed algorithms provide adequate initialization of the pose parameters as well as pose estimate in the tracking phase with 0.1 ° accuracy in attitude and sub-centimeter accuracy in relative position , operating with sparse point clouds . Finally , the effect of target visibility in the sensor field of view on the tracking algorithm performance is analyzed in detail .
In addition to maintaining a sparse map of landmarks in the world , our system finds as many inter-frame point matches as possible . These point matches provide additional constraints on the inter-frame motion of the camera leading to a more accurate pose estimate , and , since they are not maintained as full map landmarks , they do not cause a large increase in the computational cost . Our results in both a simulated environment and in real video demonstrate the improvement in estimation accuracy gained by the inclusion of visual odometry style observations .
In this paper , according to the existing problem of the traditional pose estimation method based on color image : manual intervention will bring the uncertainty leading to inaccurate foreground / background segmentation , and have a negative impact on the subsequent parsing process . We put forward a method which combined with the depth information captured by the Kinect , with the pre-processing of depth image , to improve pose estimation process in static images , so that the subsequent processing can be more convenient , and meanwhile the accuracy can be improved .
Therefore , we extend the original voting approach of ISM and let all visual words that contribute to a person hypothesis also vote for the positions of the person 's body parts . Since this approach is not constrained to a certain feature type and different feature types can even be fused during the pose estimation process , the approach is highly flexible . We show preliminary evaluation results of our approach using on the public available HumanEva dataset which comprises ground-truth pose data and thereby provides training and evaluation data .
6 ) . In the context of action recognition the pose estimation step is often replaced by the calculation of a motion descriptor .
We propose a number of different variants of our approach , including sparse encoding and ridge-regression for feature representation . While classical pose specific methods strongly depend on the quality of the pose estimation step , our approaches maintain their superior behavior even under severe pose noise . We evaluate on both BU3DFE and Multi-PIE datasets and outperform the state-of-the-art in classification accuracy , even with a simple pose specific baseline method , while being extremely robust to feature noise and erroneous viewpoint estimation with our pairwise regression approaches .
The problem is caused by the identity mapping . Since Hourglass network , also called conv - deconv structure , is an effective structure for pose estimation , object detection , and pixel level tasks , we use it as the basic structure in experiments . We observe a problem of using residual unit for Hourglass : when outputs of two residual units are summed up , the output variance is approximately doubled , which causes difficulty in optimization .
We present empirical tests on common in - the - wild pose benchmark datasets which show state - of - the - art results . Additionally we test our method on a dataset usually used for pose estimation using depth and start to close the gap with state - of - the - art depth pose methods . We open - source our training and testing code as well as release our pre - trained models .
section : Introduction The successes of deep convolutional neural nets ( CNNs ) at image classification has spawned a flurry of work in computer vision on adapting these models to pixel - level image understanding tasks , such as boundary detection , semantic segmentation , optical flow , and pose estimation . The key ideas that have enabled this adaption thus far are : ( 1 ) deconvolution schemes that allow for upsampling coarse pooled feature maps to make detailed predictions at the spatial resolution of individual pixels , ( 2 ) skip connections and hyper - columns which concatenate representations across multi - resolution feature maps , ( 3 ) atrous convolution which allows efficient computation with large receptive fields while maintaining spatial resolution , and ( 4 ) fully convolutional operation which handles variable sized input images .
The person detection counts the number of persons subsequently followed by the person tracking which determines who is stopping and who is moving . The head pose estimation denotes whether they are looking or not at the target scene . Finally the projection of the visual field extracts the location of interest in the target scene .
Head pose estimation from low resolution video can be a difficult task due to the limited quantity and quality of pixels which can be harvested for information . In this work , we propose a method for tracking change in head pose by solving a texture detection problem in an orientation space .
In addition , the proposed switching dynamic model gives better results than standard tracking models such as Kalman filter . While being applied to the head-pose estimation problem the methodology presented in this thesis is really general and can be used to solve various regression and tracking problems , e.g. we applied it to the tracking of a sound source in an image .
Fatigue monitoring can effectively reduce or even avoid traffic accidents . Head posture estimation is one of the focuses in the field of fatigue monitoring . In this paper , according to the coordina ...
Our HiBall Tracking System generates over 2000 head-pose estimates per second with less than one millisecond of latency , and less than 0.5 millimeters and 0.02 degrees of position and orientation noise , everywhere in a 4.5 by 8.5 meter room . The system is remarkably responsive and robust , enabling VR applications and experiments that previously would have been difficult or even impossible .
We address the problem of automated discretization for continuous labels in the context of head pose estimation from overhead cameras . Due to the lack of visual detail , precise head pose estimates are not always possible . A common approach is to discretize the space of head pose angles , turning a real-valued prediction task into a coarser ( ordered ) classification variant .
Even humans can have trouble without contextual information . In this work , we couple novel contextual information , social grouping , with two important computer vision tasks : multi-target tracking and head pose/direction estimation in surveillance video . These three components are modeled in a probabilistic formulation and we provide effective solvers .
By using kernel tricks , the authors implicitly map the input data into the kernel feature space associated with the kernel function . In experiments , the proposed algorithm was applied to a head pose estimation application . Experimental results demonstrated the increased effectiveness and robustness of the method .
Head pose estimation bears importance in several applications in biometrics , human-computer interfaces , driver monitoring systems , video conferencing and social interaction enhancement programs . A recent trend in head pose estimation research has been the use of manifold learning techniques to capture the underlying geometry of the images . Face images with varying pose angles can be considered to be lying on a smooth low-dimensional manifold in high-dimensional image feature space .
[ Age estimation ] [ Head pose estimation ] [ Multi - label classification ]
We demonstrate that a direct , holistic approach to estimating 3D head pose from image intensities using convolutional neural networks delivers superior accuracy in comparison to keypoint - based methods . While keypoint detectors have recently improved dramatically due to deep learning , head pose recovery inherently is a two step process with numerous opportunities for error . First , if sufficient keypoints fail to be detected , then pose recovery is impossible .
While other works have addressed the direct regression of pose from images using conv - nets they did not include a comprehensive set of benchmarks or leverage modern deep architectures . In applications where accurate head pose estimation is required , a common solution is to utilize RGBD ( depth ) cameras . These can be very accurate , but suffer from a number of limitations :
section : RELATED WORK Human head pose estimation is a widely studied task in computer vision with very diverse approaches throughout its history . In the classic literature we can discern Appearance Template Models which seek to compare test images with a set of pose exemplars .
Recently , facial landmark detectors which have become very accurate , have been popular for the task of pose estimation . Also recently , work has developed on estimating head pose using neural networks . presents an in - depth study of relatively shallow networks trained using a regression loss on the AFLW dataset .
We compare our results to all of these works . Chang et al . also argue for landmark - free head pose estimation . They regress 3D head pose using a simple CNN and focus on facial alignment using the predicted head pose .
Analysis.- Coefficient-Tracking Speckle Reducing Anisotropic Diffusion.- Increasing Computational Redundancy of Digital Images via Multiresolutional Matching.- Efficient Removal of Noisy Borders of Monochromatic Documents.- Image Segmentation.- A Visual Perception Approach for Accurate Segmentation of Light Profiles.- Implicit Active-Contouring with MRF.- A Novel Pixon-Based Approach for Image Segmentation Using Wavelet Thresholding Method.- Interactive Image Segmentation Based on Hierarchical Graph-Cut Optimization with Generic Shape Prior.- Multiphase Segmentation Based on Implicit Active Shape Models.- Image and Video Retrieval and Indexing.- Weighted Block Matching-Based Anchor Shot Detection with Dynamic Background.- Local Triplet Pattern for Content-Based Image Retrieval.- An Interactive Image Feature Visualization System for Supporting CBIR Study.- Pattern Analysis and Recognition.- Cue Integration for Urban Area Extraction in Remote Sensing Images.- Scale Invariant Feature Transform with Irregular Orientation Histogram Binning.- Affine Invariant-Based Classification of Inliers and Outliers for Image Matching.- A New Large-Scale Multi-purpose Handwritten Farsi Database.- Memory Efficient Vision Based Line Feature Extraction for Tiny Mobile Robots.- A Swift and Memory Efficient Hough Transform for Systems with Limited Fast Memory.- Novel Framework for Selecting the Optimal Feature Vector from Large Feature Spaces.- Granular Computing and Rough Sets to Generate Fuzzy Rules.- 3D Human Pose Estimation from Static Images Using Local Features and Discriminative Learning.- Abnormal Behavior Recognition Using Self-Adaptive Hidden Markov Models.- Interacting with Digital Signage Using Hand Gestures.- Probability Error in Bayes Optimal Classifier with Intuitionistic Fuzzy Observations.- Fuzzy Gaussian Process Classification Model.- VizDraw : A Platform to Convert Online Hand-Drawn Graphics into Computer Graphics.- Biometrics.- Score Level Fusion of Ear and Face Local 3D Features for Fast and Expression-Invariant Human Recognition.- Speech-Video Synchronization Using Lips Movements and Speech Envelope Correlation.- Analyzing Human Gait Using Patterns of Translation and Rotation.- Level Set Approaches and Adaptive Asymmetrical SVMs Applied for Nonideal Iris Recognition.- A Security Analysis of Biometric Template Protection
While deep learning has lead to great advances in 2D and 3D pose estimation from single video sources , the problem of estimating 3D human pose from multiple video sensors with overlapping fields of view has received less attention . When the application allows use of multiple cameras , 3D human pose estimates may be greatly improved through fusion of multi-view pose estimates and observation of limbs that are fully or partially occluded in some views . Past approaches to multi-view 3D pose estimation have used probabilistic graphical models to reason over constraints , including per-image pose estimates , temporal smoothness , and limb length .
We propose a generative statistical approach to human motion modeling and tracking that utilizes probabilistic latent semantic ( PLSA ) models to describe the mapping of image features to 3D human pose estimates . PLSA has been successfully used to model the co-occurrence of dyadic data on problems such as image annotation where image features are mapped to word categories via latent variable semantics .
Abstract 3D human pose estimation from a single image is a challenging problem due to occlusion , viewpoint variance , and the ill-posed nature of back projection . We follow a standard two-step pipeline which first detects 2D joint locations and uses them to infer 3D pose .
Abstract 3D human pose estimation from 2D joints of an image is a worthwhile and challenging research topic . Since a specific 2D pose could be projected from various 3D poses , the ambiguity becomes a difficult obstacle when recovering 3D pose from 2D .
This system has three main hybrid algorithms , namely 2D/3D human pose pre-processing algorithm , ensemble graph chaining segmented model on 2D/3D video sequence pose estimation and 3D ensemble facial expression detection algorithm . The experimental results on public benchmarks 2D/3D datasets show that our model is more efficient in solving arbitrary human pose estimation problem . Also , this model has the high true positive rate , low false detection rate compared to traditional joint human pose detection models .
Abstract Automatic identification of various facial movements and expressions with high recognition value is important for human computer interaction as the facial behavior of a human can be treated as an important factor for information representation as well as communication . A high deviation of human appearance and existence of noisy contextual background makes the human pose analysis is hard to achieve . A number of basic factors such as cluttered background , occlusion , and camera movement and illumination variations degrade the image quality resulting in poor performance for identifying different facial expressions .
Human pose analysis has been known to be an effective means to evaluate athlete 's performance . Marker-less 3D human pose estimation is one of the most practical methods to acquire human pose but lacks sufficient accuracy required to achieve precise performance analysis for sports .
To ensure temporal consistency in the refining stage , we adopt the ranking loss for adversarial training . We conduct extensive experiments on two image-to-video translation tasks : facial expression retargeting and human pose forecasting . Superior results over the state of the art on both tasks demonstrate the effectiveness of our approach .
Inspired by the recent success of deep reinforcement learning methods , in this paper we propose a new reinforcement learning formulation for the problem of human pose prediction , and develop an imitation learning algorithm for predicting future poses under this formulation through a combination of behavioral cloning and generative adversarial imitation learning . Our experiments show that our proposed method outperforms all existing state-of-the-art baseline models by large margins on the task of human pose prediction in both short-term predictions and long-term predictions , while also enjoying huge advantage in training speed .
Significant progress has been made based on the development of depth sensors , i.e. , accessible human pose prediction from still depth images~\cite{rf12pami}. However , most of the existing approaches to this problem involve several components/models that are independently designed and optimized , leading to suboptimal performances . In this paper , we propose a novel inference-embedded multi-task learning framework for predicting human pose from still depth images , which is implemented with a deep architecture of neural networks . Specifically , we handle two cascaded tasks : i ) generating the heat ( confidence ) maps of body parts via a fully convolutional network ( FCN ) ; ii ) seeking the optimal configuration of body parts based on the detected body part proposals via an inference built-in MatchNet~\cite{mn15cvpr } , which measures the appearance and geometric kinematic compatibility of body parts and embodies the dynamic programming inference as an extra network layer .
Predicting human pose in the wild is a challenging problem due to high flexibility of joints and possible occlusion . Existing approaches generally tackle the difficulties either by holistic prediction or multi-stage processing , which suffer from poor performance for locating challenging joints or high computational cost .
Improved CNN supervision leverages first and second order parent relationships along the skeletal kinematic tree , and improved multi-level skip connections to learn better representations through implicit modification of the loss landscape . Further , transfer learning from 2D human pose prediction significantly improves accuracy and generalizability to unseen poses and camera views . Additionally , we contribute a new benchmark and training set for human body pose estimation from monocular images of real humans , that has ground truth captured with marker-less motion capture .
Moreover , if the predicted future is too short ( e.g. , less than one second ) , it may not be fully usable by a human or other systems . In this paper , we propose a novel method for future human pose prediction capable of predicting multiple long-term futures . This makes the predictions more suitable for real applications .
This also allows us to directly generate novel images from the trained model by sampling from the discovered latent spaces . We also demonstrate the model by prediction of human pose in an ambiguous setting . Our Bayesian framework allows us to perform disambiguation in a principled manner by including latent space priors which incorporate the dynamic nature of the data .
Recently , it has been shown that similar refinement can be obtained by stacking several convolutional networks on top of each other . This led to improved results in human pose estimation and semantic instance segmentation . In this paper we adapt the idea of stacking multiple networks to optical flow estimation .
Given the input shape , the goal of is to regress the shape primitive parameters which is achieved via a CNN . The method of extends classical work on heatmap regression by proposing a 4D representation for regressing the location of sparse 3D landmarks for human pose estimation . Different from , we demonstrate that a 3D volumetric representation is particular effective for learning dense 3D facial geometry .
While recent advances in pose estimation have improved performance on front and side views , most real - world settings present challenging viewpoints such as top or angled views in retail stores , hospital environments , or airport settings . These viewpoints introduce high levels of self - occlusion making human pose estimation difficult for existing algorithms . Humans are remarkably robust at predicting full rigid - body and articulated poses in these challenging scenarios .
Synthetic ground - truth can be established by rendering images using surface - based models . This has recently been applied to human pose in the SURREAL dataset of , where the SMPL model was rendered with the CMU Mocap dataset poses . However , covariate shift can emerge because of the different statistics of rendered and natural images .
Recent pose estimation systems have universally adopted ConvNets as their main building block , largely replacing hand - crafted features and graphical models ; this strategy has yielded drastic improvements on standard benchmarks . We continue along this trajectory and introduce a novel “ stacked hourglass ” network design for predicting human pose . The network captures and consolidates information across all scales of the image .
section : Conclusion We demonstrate the effectiveness of a stacked hourglass network for producing human pose estimates . The network handles a diverse and challenging set of poses with a simple mechanism for reevaluation and assessment of initial predictions .
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