Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Combined Methods <s> This article presents a mathematical paradigm called Data Driven Markov Chain Monte Carlo (DDMCMC) for object recognition. The objectives of this paradigm are two-fold. Firstly, it realizes traditional "hypothesis-and-test" methods through well-balanced Markov chain Monte Carlo (MCMC) dynamics, thus it achieves robust and globally optimal solutions. Secondly, it utilizes data-driven (bottom-up) methods in computer vision, such as Hough transform and data clustering, to design effective transition probabilities for Markov chain dynamics. This drastically improves the effectiveness of traditional MCMC algorithms in terms of two standard metrics: "burn-in" period and "mixing" rate. The article proceeds in three steps. Firstly, we analyze the structures of the solution space /spl Omega/ for object recognition. /spl Omega/ is decomposed into a large number of subspaces of varying dimensions in a hierarchy. Secondly, we use data-driven techniques to compute importance proposal probabilities in these spaces, each expressed in a non-parametric form using weighted samples or particles. Thirdly, Markov chains are designed to travel in such heterogeneous structured solution space, with both jump and diffusion dynamics. We use possibly the simplest objects-the "/spl Psi/-world" as an example to illustrate the concepts, and we briefly present results on an application of traffic sign detection. <s> BIB001 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Combined Methods <s> In This work we propose a statistical model for detection and tracking of human silhouette and the corresponding 3D skeletal structure in gait sequences. We follow a point distribution model (PDM) approach using a principal component analysis (PCA). The problem of non-linear PCA is partially resolved by applying a different PDM depending of pose estimation; frontal, lateral and diagonal, estimated by Fisher's linear discriminant. Additionally, the fitting is carried out by selecting the closest allowable shape from the training set by means of a nearest neighbor classifier. To improve the performance of the model we develop a human gait analysis to take into account temporal dynamic to track the human body. The incorporation of temporal constraints on the model helps increase the reliability and robustness. <s> BIB002 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Combined Methods <s> We address 3D human motion capture from monocular images, taking a learning based approach to construct a probabilistic pose estimation model from a set of labelled human silhouettes. To compensate for ambiguities in the pose reconstruction problem, our model explicitly calculates several possible pose hypotheses. It uses locality on a manifold in the input space and connectivity in the output space to identify regions of multi-valuedness in the mapping from silhouette to 3D pose. This information is used to fit a mixture of regressors on the input manifold, giving us a global model capable of predicting the possible poses with corresponding probabilities. These are then used in a dynamicalmodel based tracker that automatically detects tracking failures and re-initializes in a probabilistically correct manner. The system is trained on conventional motion capture data, using both the corresponding real human silhouettes and silhouettes synthesized artificially from several different models for improved robustness to inter-person variations. Static pose estimation is illustrated on a variety of silhouettes. The robustness of the method is demonstrated by tracking on a real image sequence requiring multiple automatic re-initializations. <s> BIB003 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Combined Methods <s> We develop a method for the estimation of articulated pose, such as that of the human body or the human hand, from a single (monocular) image. Pose estimation is formulated as a statistical inference problem, where the goal is to find a posterior probability distribution over poses as well as a maximum a posteriori (MAP) estimate. The method combines two modeling approaches, one discriminative and the other generative. The discriminative model consists of a set of mapping functions that are constructed automatically from a labeled training set of body poses and their respective image features. The discriminative formulation allows for modeling ambiguous, one-to-many mappings (through the use of multi-modal distributions) that may yield multiple valid articulated pose hypotheses from a single image. The generative model is defined in terms of a computer graphics rendering of poses. While the generative model offers an accurate way to relate observed (image features) and hidden (body pose) random variables, it is difficult to use it directly in pose estimation, since inference is computationally intractable. In contrast, inference with the discriminative model is tractable, but considerably less accurate for the problem of interest. A combined discriminative/generative formulation is derived that leverages the complimentary strengths of both models in a principled framework for articulated pose inference. Two efficient MAP pose estimation algorithms are derived from this formulation; the first is deterministic and the second non-deterministic. Performance of the framework is quantitatively evaluated in estimating articulated pose of both the human hand and human body. <s> BIB004 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Combined Methods <s> Estimation of three-dimensional articulated human pose and motion from images is a central problem in computer vision. Much of the previous work has been limited by the use of crude generative models of humans represented as articulated collections of simple parts such as cylinders. Automatic initialization of such models has proved difficult and most approaches assume that the size and shape of the body parts are known a priori. In this paper we propose a method for automatically recovering a detailed parametric model of non-rigid body shape and pose from monocular imagery. Specifically, we represent the body using a parameterized triangulated mesh model that is learned from a database of human range scans. We demonstrate a discriminative method to directly recover the model parameters from monocular images using a conditional mixture of kernel regressors. This predicted pose and shape are used to initialize a generative model for more detailed pose and shape estimation. The resulting approach allows fully automatic pose and shape recovery from monocular and multi-camera imagery. Experimental results show that our method is capable of robustly recovering articulated pose, shape and biometric measurements (e.g. height, weight, etc.) in both calibrated and uncalibrated camera environments. <s> BIB005 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Combined Methods <s> In this paper we consider modeling data lying on multiple continuous manifolds. In particular, we model the shape manifold of a person performing a motion observed from different view points along a view circle at fixed camera height. We introduce a model that ties together the body configuration (kinematics) manifold and the visual manifold (observations) in a way that facilitates tracking the 3D configuration with continuous relative view variability. The model exploits the low dimensionality nature of both the body configuration manifold and the view manifold where each of them are represented separately. <s> BIB006 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Combined Methods <s> 3D human pose estimation in multi-view settings benefits from embeddings of human actions in low-dimensional manifolds, but the complexity of the embeddings increases with the number of actions. Creating separate, action-specific manifolds seems to be a more practical solution. Using multiple manifolds for pose estimation, however, requires a joint optimization over the set of manifolds and the human pose embedded in the manifolds. In order to solve this problem, we propose a particle-based optimization algorithm that can efficiently estimate human pose even in challenging in-house scenarios. In addition, the algorithm can directly integrate the results of a 2D action recognition system as prior distribution for optimization. In our experiments, we demonstrate that the optimization handles an 84D search space and provides already competitive results on HumanEva with as few as 25 particles. <s> BIB007 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Combined Methods <s> In this paper, we address the recovery of human 2D postures from monocular image sequences. We propose a novel pose estimation framework which is based on the integration of probabilistic bottom-up and top-down processes which iteratively refine each other: foreground pixels are segmented using image cues whereas a hierarchical 2D body model fitting constraints body partitions. Its main advantages are twofold. First, the presented framework is activity-independent since it does not rely on learning any motion model. Secondly, we propose a confidence score indicating the quality of each estimated pose. Our study also reveals significant discrepancy between ground truth joint positions according to whether they are defined by humans or a motion capture system. Quantitative and qualitative results are presented on a variety of video sequences to validate our approach. <s> BIB008 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Combined Methods <s> Until recently Intelligence, Surveillance, and Reconnaissance (ISR) focused on acquiring behavioral information of the targets and their activities. Continuous evolution of intelligence being gathered of the human centric activities has put increased focus on the humans, especially inferring their innate characteristics - size, shapes and physiology. These bio-signatures extracted from the surveillance sensors can be used to deduce age, ethnicity, gender and actions, and further characterize human actions in unseen scenarios. However, recovery of pose and shape of humans in such monocular videos is inherently an ill-posed problem, marked by frequent depth and view based ambiguities due to self-occlusion, foreshortening and misalignment. The likelihood function often yields a highly multimodal posterior that is difficult to propagate even using the most advanced particle filtering(PF) algorithms. Motivated by the recent success of the discriminative approaches to efficiently predict 3D poses directly from the 2D images, we present several principled approaches to integrate predictive cues using learned regression models to sustain multimodality of the posterior during tracking. Additionally, these learned priors can be actively adapted to the test data using a likelihood based feedback mechanism. Estimated 3D poses are then used to fit 3D human shape model to each frame independently for inferring anthropometric bio-signatures. The proposed system is fully automated, robust to noisy test data and has ability to swiftly recover from tracking failures even after confronting with significant errors. We evaluate the system on a large number of monocular human motion sequences. <s> BIB009
Combined Methods of discriminative and generative methods: BIB007 BIB002 BIB005 BIB003  BIB006 BIB001 BIB008 BIB009 BIB004 Top-Down Methods Top-Down Methods:  Bottom-Up Methods