Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> Our paper addresses the problem of enforcing constraints in human body tracking. A projection technique is derived to impose kinematic constraints on independent multibody motion: we show that for small motions the multibody articulated motion space can be approximated by a linear manifold estimated directly from the previous body pose. We propose a learning approach to model nonlinear constraints; we train a support vector classifier from motion capture data to model the boundary of the space of valid poses. Linear and nonlinear body pose constraints are enforced by first projecting unconstrained motions onto the articulated motion space and then optimizing to find points on this linear manifold that lie within the non-linear constraint surface modeled by the SVM classifier. <s> BIB001 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> In this paper we present a computationally efficient framework for part-based modeling and recognition of objects. Our work is motivated by the pictorial structure models introduced by Fischler and Elschlager. The basic idea is to represent an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. We address the problem of using pictorial structure models to find instances of an object in an image as well as the problem of learning an object model from training examples, presenting efficient algorithms in both cases. We demonstrate the techniques by learning models that represent faces and human bodies and using the resulting models to locate the corresponding objects in novel images. <s> BIB002 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> Tree-structured models have been widely used for human pose estimation, in either 2D or 3D. While such models allow efficient learning and inference, they fail to capture additional dependencies between body parts, other than kinematic constraints between connected parts. In this paper, we consider the use of multiple tree models, rather than a single tree model for human pose estimation. Our model can alleviate the limitations of a single tree-structured model by combining information provided across different tree models. The parameters of each individual tree model are trained via standard learning algorithms in a single tree-structured model. Different tree models can be combined in a discriminative fashion by a boosting procedure. We present experimental results showing the improvement of our approaches on two different datasets. On the first dataset, we use our multiple tree framework for occlusion reasoning. On the second dataset, we combine multiple deformable trees for capturing spatial constraints between non-connected body parts. <s> BIB003 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> Non-rigid object detection and articulated pose estimation are two related and challenging problems in computer vision. Numerous models have been proposed over the years and often address different special cases, such as pedestrian detection or upper body pose estimation in TV footage. This paper shows that such specialization may not be necessary, and proposes a generic approach based on the pictorial structures framework. We show that the right selection of components for both appearance and spatial modeling is crucial for general applicability and overall performance of the model. The appearance of body parts is modeled using densely sampled shape context descriptors and discriminatively trained AdaBoost classifiers. Furthermore, we interpret the normalized margin of each classifier as likelihood in a generative model. Non-Gaussian relationships between parts are represented as Gaussians in the coordinate system of the joint between parts. The marginal posterior of each part is inferred using belief propagation. We demonstrate that such a model is equally suitable for both detection and pose estimation tasks, outperforming the state of the art on three recently proposed datasets. <s> BIB004 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> We investigate the task of 2D articulated human pose estimation in unconstrained still images. This is extremely challenging because of variation in pose, anatomy, clothing, and imaging conditions. Current methods use simple models of body part appearance and plausible configurations due to limitations of available training data and constraints on computational expense. We show that such models severely limit accuracy. Building on the successful pictorial structure model (PSM) we propose richer models of both appearance and pose, using state-of-the-art discriminative classifiers without introducing unacceptable computational expense. We introduce a new annotated database of challenging consumer images, an order of magnitude larger than currently available datasets, and demonstrate over 50% relative improvement in pose estimation accuracy over a stateof-the-art method. <s> BIB005 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL data sets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI--SVM in terms of latent variables. A latent SVM is semiconvex, and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function. <s> BIB006 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> Automatic recovery of 3D human pose from monocular image sequences is a challenging and important research topic with numerous applications. Although current methods are able to recover 3D pose for a single person in controlled environments, they are severely challenged by real-world scenarios, such as crowded street scenes. To address this problem, we propose a three-stage process building on a number of recent advances. The first stage obtains an initial estimate of the 2D articulation and viewpoint of the person from single frames. The second stage allows early data association across frames based on tracking-by-detection. These two stages successfully accumulate the available 2D image evidence into robust estimates of 2D limb positions over short image sequences (= tracklets). The third and final stage uses those tracklet-based estimates as robust image observations to reliably recover 3D pose. We demonstrate state-of-the-art performance on the HumanEva II benchmark, and also show the applicability of our approach to articulated 3D tracking in realistic street conditions. <s> BIB007 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> Pictorial structure (PS) models are extensively used for part-based recognition of scenes, people, animals and multi-part objects. To achieve tractability, the structure and parameterization of the model is often restricted, for example, by assuming tree dependency structure and unimodal, data-independent pairwise interactions. These expressivity restrictions fail to capture important patterns in the data. On the other hand, local methods such as nearest-neighbor classification and kernel density estimation provide non-parametric flexibility but require large amounts of data to generalize well. We propose a simple semi-parametric approach that combines the tractability of pictorial structure inference with the flexibility of non-parametric methods by expressing a subset of model parameters as kernel regression estimates from a learned sparse set of exemplars. This yields query-specific, image-dependent pose priors. We develop an effective shape-based kernel for upper-body pose similarity and propose a leave-one-out loss function for learning a sparse subset of exemplars for kernel regression. We apply our techniques to two challenging datasets of human figure parsing and advance the state-of-the-art (from 80% to 86% on the Buffy dataset [8]), while using only 15% of the training data as exemplars. <s> BIB008 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> We address the problem of articulated human pose estimation by learning a coarse-to-fine cascade of pictorial structure models. While the fine-level state-space of poses of individual parts is too large to permit the use of rich appearance models, most possibilities can be ruled out by efficient structured models at a coarser scale. We propose to learn a sequence of structured models at different pose resolutions, where coarse models filter the pose space for the next level via their max-marginals. The cascade is trained to prune as much as possible while preserving true poses for the final level pictorial structure model. The final level uses much more expensive segmentation, contour and shape features in the model for the remaining filtered set of candidates. We evaluate our framework on the challenging Buffy and PASCAL human pose datasets, improving the state-of-the-art. <s> BIB009 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> We describe a method for human pose estimation in static images based on a novel representation of part models. Notably, we do not use articulated limb parts, but rather capture orientation with a mixture of templates for each part. We describe a general, flexible mixture model for capturing contextual co-occurrence relations between parts, augmenting standard spring models that encode spatial relations. We show that such relations can capture notions of local rigidity. When co-occurrence and spatial relations are tree-structured, our model can be efficiently optimized with dynamic programming. We present experimental results on standard benchmarks for pose estimation that indicate our approach is the state-of-the-art system for pose estimation, outperforming past work by 50% while being orders of magnitude faster. <s> BIB010 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> The task of 2-D articulated human pose estimation in natural images is extremely challenging due to the high level of variation in human appearance. These variations arise from different clothing, anatomy, imaging conditions and the large number of poses it is possible for a human body to take. Recent work has shown state-of-the-art results by partitioning the pose space and using strong nonlinear classifiers such that the pose dependence and multi-modal nature of body part appearance can be captured. We propose to extend these methods to handle much larger quantities of training data, an order of magnitude larger than current datasets, and show how to utilize Amazon Mechanical Turk and a latent annotation update scheme to achieve high quality annotations at low cost. We demonstrate a significant increase in pose estimation accuracy, while simultaneously reducing computational expense by a factor of 10, and contribute a dataset of 10,000 highly articulated poses. <s> BIB011 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> Despite recent successes, pose estimators are still somewhat fragile, and they frequently rely on a precise knowledge of the location of the object. Unfortunately, articulated objects are also very difficult to detect. Knowledge about the articulated nature of these objects, however, can substantially contribute to the task of finding them in an image. It is somewhat surprising, that these two tasks are usually treated entirely separately. In this paper, we propose an Articulated Part-based Model (APM) for jointly detecting objects and estimating their poses. APM recursively represents an object as a collection of parts at multiple levels of detail, from coarse-to-fine, where parts at every level are connected to a coarser level through a parent-child relationship (Fig. 1(b)-Horizontal). Parts are further grouped into part-types (e.g., left-facing head, long stretching arm, etc) so as to model appearance variations (Fig. 1(b)-Vertical). By having the ability to share appearance models of part types and by decomposing complex poses into parent-child pairwise relationships, APM strikes a good balance between model complexity and model richness. Extensive quantitative and qualitative experiment results on public datasets show that APM outperforms state-of-the-art methods. We also show results on PASCAL 2007 - cats and dogs - two highly challenging articulated object categories. <s> BIB012 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> We consider the problem of human parsing with part-based models. Most previous work in part-based models only considers rigid parts (e.g. torso, head, half limbs) guided by human anatomy. We argue that this representation of parts is not necessarily appropriate for human parsing. In this paper, we introduce hierarchical poselets–a new representation for human parsing. Hierarchical poselets can be rigid parts, but they can also be parts that cover large portions of human bodies (e.g. torso + left arm). In the extreme case, they can be the whole bodies. We develop a structured model to organize poselets in a hierarchical way and learn the model parameters in a max-margin framework. We demonstrate the superior performance of our proposed approach on two datasets with aggressive pose variations. <s> BIB013 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> This paper introduces a new rigorous theoretical framework to address discrete MRF-based optimization in computer vision. Such a framework exploits the powerful technique of Dual Decomposition. It is based on a projected subgradient scheme that attempts to solve an MRF optimization problem by first decomposing it into a set of appropriately chosen subproblems, and then combining their solutions in a principled way. In order to determine the limits of this method, we analyze the conditions that these subproblems have to satisfy and demonstrate the extreme generality and flexibility of such an approach. We thus show that by appropriately choosing what subproblems to use, one can design novel and very powerful MRF optimization algorithms. For instance, in this manner we are able to derive algorithms that: 1) generalize and extend state-of-the-art message-passing methods, 2) optimize very tight LP-relaxations to MRF optimization, and 3) take full advantage of the special structure that may exist in particular MRFs, allowing the use of efficient inference techniques such as, e.g., graph-cut-based methods. Theoretical analysis on the bounds related with the different algorithms derived from our framework and experimental results/comparisons using synthetic and real data for a variety of tasks in computer vision demonstrate the extreme potentials of our approach. <s> BIB014 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> Pictorial Structures (PS) define a probabilistic model of 2D articulated objects in images. Typical PS models assume an object can be represented by a set of rigid parts connected with pairwise constraints that define the prior probability of part configurations. These models are widely used to represent non-rigid articulated objects such as humans and animals despite the fact that such objects have parts that deform non-rigidly. Here we define a new Deformable Structures (DS) model that is a natural extension of previous PS models and that captures the non-rigid shape deformation of the parts. Each part in a DS model is represented by a low-dimensional shape deformation space and pairwise potentials between parts capture how the shape varies with pose and the shape of neighboring parts. A key advantage of such a model is that it more accurately models object boundaries. This enables image likelihood models that are more discriminative than previous PS likelihoods. This likelihood is learned using training imagery annotated using a DS “puppet.” We focus on a human DS model learned from 2D projections of a realistic 3D human body model and use it to infer human poses in images using a form of non-parametric belief propagation. <s> BIB015 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> Human pose estimation requires a versatile yet well-constrained spatial model for grouping locally ambiguous parts together to produce a globally consistent hypothesis. Previous works either use local deformable models deviating from a certain template, or use a global mixture representation in the pose space. In this paper, we propose a new hierarchical spatial model that can capture an exponential number of poses with a compact mixture representation on each part. Using latent nodes, it can represent high-order spatial relationship among parts with exact inference. Different from recent hierarchical models that associate each latent node to a mixture of appearance templates (like HoG), we use the hierarchical structure as a pure spatial prior avoiding the large and often confounding appearance space. We verify the effectiveness of this model in three ways. First, samples representing human-like poses can be drawn from our model, showing its ability to capture high-order dependencies of parts. Second, our model achieves accurate reconstruction of unseen poses compared to a nearest neighbor pose representation. Finally, our model achieves state-of-art performance on three challenging datasets, and substantially outperforms recent hierarchical models. <s> BIB016 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> In this paper, we propose a novel and integrated framework to estimate human pose. Firstly, a pose cluster of the relative location between connected parts is applied before pictorial structure modeling, which can make each model more faithful and the whole estimation more flexible to various kinds of poses. And then, different from previous single global model, we propose the mixture pictorial structure models based on the clusters to obtain the parts candidates. Furthermore, to overcome the double-counting problem, we also present a constraint function to recombine the candidates derived from the optimal clustered model. Experiments on a publicly challenging dataset show that our method is more accurate and flexible and performs effectively in tackling the double-counting phenomena. <s> BIB017 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> Having a sensible prior of human pose is a vital ingredient for many computer vision applications, including tracking and pose estimation. While the application of global non-parametric approaches and parametric models has led to some success, finding the right balance in terms of flexibility and tractability, as well as estimating model parameters from data has turned out to be challenging. In this work, we introduce a sparse Bayesian network model of human pose that is non-parametric with respect to the estimation of both its graph structure and its local distributions. We describe an efficient sampling scheme for our model and show its tractability for the computation of exact log-likelihoods. We empirically validate our approach on the Human 3.6M dataset and demonstrate superior performance to global models and parametric networks. We further illustrate our model's ability to represent and compose poses not present in the training set (compositionality) and describe a speed-accuracy trade-off that allows real-time scoring of poses. <s> BIB018 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> The human body is structurally symmetric. Tracking by detection approaches for human pose suffer from double counting, where the same image evidence is used to explain two separate but symmetric parts, such as the left and right feet. Double counting, if left unaddressed can critically affect subsequent processes, such as action recognition, affordance estimation, and pose reconstruction. In this work, we present an occlusion aware algorithm for tracking human pose in an image sequence, that addresses the problem of double counting. Our key insight is that tracking human pose can be cast as a multi-target tracking problem where the ”targets” are related by an underlying articulated structure. The human body is modeled as a combination of singleton parts (such as the head and neck) and symmetric pairs of parts (such as the shoulders, knees, and feet). Symmetric body parts are jointly tracked with mutual exclusion constraints to prevent double counting by reasoning about occlusion. We evaluate our algorithm on an outdoor dataset with natural background clutter, a standard indoor dataset (HumanEva-I), and compare against a state of the art pose estimation algorithm. <s> BIB019 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> We propose a multimodal, decomposable model for articulated human pose estimation in monocular images. A typical approach to this problem is to use a linear structured model, which struggles to capture the wide range of appearance present in realistic, unconstrained images. In this paper, we instead propose a model of human pose that explicitly captures a variety of pose modes. Unlike other multimodal models, our approach includes both global and local pose cues and uses a convex objective and joint training for mode selection and pose estimation. We also employ a cascaded mode selection step which controls the trade-off between speed and accuracy, yielding a 5x speedup in inference and learning. Our model outperforms state-of-the-art approaches across the accuracy-speed trade-off curve for several pose datasets. This includes our newly-collected dataset of people in movies, FLIC, which contains an order of magnitude more labeled data for training and testing than existing datasets. <s> BIB020 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> We present a method for estimating articulated human pose from a single static image based on a graphical model with novel pairwise relations that make adaptive use of local image measurements. More precisely, we specify a graphical model for human pose which exploits the fact the local image measurements can be used both to detect parts (or joints) and also to predict the spatial relationships between them (Image Dependent Pairwise Relations). These spatial relationships are represented by a mixture model. We use Deep Convolutional Neural Networks (DCNNs) to learn conditional probabilities for the presence of parts and their spatial relationships within image patches. Hence our model combines the representational flexibility of graphical models with the efficiency and statistical power of DCNNs. Our method significantly outperforms the state of the art methods on the LSP and FLIC datasets and also performs very well on the Buffy dataset without any training. <s> BIB021 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> In this paper, we propose an image classification method that recognizes several poses of idol photographs. The proposed method takes unannotated idol photos as input, and classifies them according to their poses based on spatial layouts of the idol in the photos. Our method has two phases, the first one is to estimate the spatial layout of ten body parts (head, torso, upper and lower arms and legs) using Eichner's Stickman Pose Estimation. The second one is to classify the poses of the idols using Bayesian Network classifiers. In order to improve accuracy of the classification, we introduce Pose Guide Ontology (PGO). PGO contains useful background knowledge, such as semantic hierarchies and constraints related to the positional relationship between the body parts. The location information of body parts is amended by PGO. We also propose iterative procedures for making further refinements of PGO. Finally, we evaluated our method on a dataset consisting of 400 images in 8 poses, and the final results indicated that F-measure of the classification has become 15% higher than non-amended results. <s> BIB022 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> Estimating 3D human pose from 2D joint locations is central to the analysis of people in images and video. To address the fact that the problem is inherently ill posed, many methods impose a prior over human poses. Unfortunately these priors admit invalid poses because they do not model how joint-limits vary with pose. Here we make two key contributions. First, we collect a motion capture dataset that explores a wide range of human poses. From this we learn a pose-dependent model of joint limits that forms our prior. Both dataset and prior are available for research purposes. Second, we define a general parametrization of body pose and a new, multi-stage, method to estimate 3D pose from 2D joint locations using an over-complete dictionary of poses. Our method shows good generalization while avoiding impossible poses. We quantitatively compare our method with recent work and show state-of-the-art results on 2D to 3D pose estimation using the CMU mocap dataset. We also show superior results using manual annotations on real images and automatic detections on the Leeds sports pose dataset. <s> BIB023 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Kinematic Model <s> This paper presents an approach to parsing humans when there is significant occlusion. We model humans using a graphical model which has a tree structure building on recent work [32, 6] and exploit the connectivity prior that, even in presence of occlusion, the visible nodes form a connected subtree of the graphical model. We call each connected subtree a flexible composition of object parts. This involves a novel method for learning occlusion cues. During inference we need to search over a mixture of different flexible models. By exploiting part sharing, we show that this inference can be done extremely efficiently requiring only twice as many computations as searching for the entire object (i.e., not modeling occlusion). We evaluate our model on the standard benchmarked “We Are Family” Stickmen dataset and obtain significant performance improvements over the best alternative algorithms. <s> BIB024
Models that follow the skeletal structure are called kinematic chain models BIB018 . The set of joint positions and limb orientations are both effective representations of a human pose. One coordinate-free representation is introduced in BIB023 : the local coordinates of the upper-arms, upper-legs, and the head can be converted into spherical coordinates, and the discretized azimuthal and polar angles of the bones can be defined. The kinematic model allows us to incorporate prior beliefs about joint angles. To achieve this, a set of joint angle training data needs to be labelled with positive and negative examples of human pose BIB001 . There are two categories of the kinematic model; one is the predefined model, and the other is the learned graph structure. A very popular graph model is pictorial structure models (PSM) BIB015 BIB005 . A special case of PSM is tree-structured models. Thanks to their unique solutions, tree-structured models are successfully applied in human pose estimation, in either 2D or 3D BIB006  BIB002 BIB004 BIB007 BIB008 BIB010 BIB024 . However, the inference is unable to capture additional dependencies between body parts, other than kinematic constraints between connected parts. For example, a kinematic tree model has its limitations in representing global balance and gravity constraints. In addition, the body parts could not be completely detected under the circumstance of partial occlusion BIB019 . Many researchers seek an improvement of tree-structured models BIB009 BIB003 BIB011 BIB016  BIB012 BIB013 BIB020 BIB017 . For instance, authors in BIB003 solve the lack of model description by adding tree-structured models with different shapes, the authors of BIB011 add the spatial constraint of unconnected body parts by changing the optimized objective function, the authors of BIB020 enhance the descriptive ability by adding the states of the models. The authors of BIB003 use multiple tree models instead of a single tree model for human pose estimation. The parameters of each individual tree model are trained via standard learning algorithms in a single tree-structured model. Another example of using multiple tree structures is BIB014 , where different tree models are combined. More general than predefined structure models, pairwise body part relations could be learned from images BIB021 . Additionally, a tree structure based on Bayesian networks could be learned BIB018 BIB022 . These models are non-parametric with respect to the estimation of both their graph structure and their local distributions.