Human Pose Estimation from Monocular Images : a Comprehensive Survey

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problema into several modules: feature extraction and description, human body models, and modelin methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used

Automatic visual detection of human behavior: a review from 2000 to 2014

Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.This work is funded by the Portuguese Foundation for Science and Technology (FCT - Fundacao para a Ciencia e a Tecnologia) under research Grant SFRH/BD/84939/2012

Model-Based High-Dimensional Pose Estimation with Application to Hand Tracking

This thesis presents novel techniques for computer vision based full-DOF human hand motion estimation. Our main contributions are: A robust skin color estimation approach; A novel resolution-independent and memory efficient representation of hand pose silhouettes, which allows us to compute area-based similarity measures in near-constant time; A set of new segmentation-based similarity measures; A new class of similarity measures that work for nearly arbitrary input modalities; A novel edge-based similarity measure that avoids any problematic thresholding or discretizations and can be computed very efficiently in Fourier space; A template hierarchy to minimize the number of similarity computations needed for finding the most likely hand pose observed; And finally, a novel image space search method, which we naturally combine with our hierarchy. Consequently, matching can efficiently be formulated as a simultaneous template tree traversal and function maximization

Review of constraints on vision-based gesture recognition for human–computer interaction

The ability of computers to recognise hand gestures visually is essential for progress in human-computer interaction. Gesture recognition has applications ranging from sign language to medical assistance to virtual reality. However, gesture recognition is extremely challenging not only because of its diverse contexts, multiple interpretations, and spatio-temporal variations but also because of the complex non-rigid properties of the hand. This study surveys major constraints on vision-based gesture recognition occurring in detection and pre-processing, representation and feature extraction, and recognition. Current challenges are explored in detail

Human Action Recognition via Fused Kinematic Structure and Surface Representation

Human action recognition from visual data has remained a challenging problem in the field of computer vision and pattern recognition. This dissertation introduces a new methodology for human action recognition using motion features extracted from kinematic structure, and shape features extracted from surface representation of human body. Motion features are used to provide sufficient information about human movement, whereas shape features are used to describe the structure of silhouette. These features are fused at the kernel level using Multikernel Learning (MKL) technique to enhance the overall performance of human action recognition. In fact, there are advantages in using multiple types of features for human action recognition, especially, if the features are complementary to each other (e.g. kinematic/motion features and shape features). For instance, challenging problems such as inter-class similarity among actions and performance variation, which cannot be resolved easily by using a single type of feature, can be handled by fusing multiple types of features. This dissertation presents a new method for representing the human body surface provided by depth map (3-D) using spherical harmonics representation. The advantage of using the spherical harmonics representation is to represent the whole body surface into a nite series of spherical harmonics coefficients. Furthermore, these series can be used to describe the pose of the body using the phase information encoded inside the coefficients. Another method for detecting/tracking distal limb segments using the kinematic structure is developed. The advantage of using the distal limb segments is to extract discriminative features that can provide sufficient and compact information to recognize human actions. Our experimental results show that the aforementioned methods for human action description are complementary to each other. Hence, combining both features can enhance the robustness of action recognition. In this context, a framework to fuse multiple features using MKL technique is developed. The experimental results show that this framework is promising in incorporating multiple features in different domain for automated recognition of human action

Vision-based 3D Pose Retrieval and Reconstruction

The people analysis and the understandings of their motions are the key components in many applications like sports sciences, biomechanics, medical rehabilitation, animated movie productions and the game industry. In this context, retrieval and reconstruction of the articulated 3D human poses are considered as the significant sub-elements. In this dissertation, we address the problem of retrieval and reconstruction of the 3D poses from a monocular video or even from a single RGB image. We propose a few data-driven pipelines to retrieve and reconstruct the 3D poses by exploiting the motion capture data as a prior. The main focus of our proposed approaches is to bridge the gap between the separate media of the 3D marker-based recording and the capturing of motions or photographs using a simple RGB camera. In principal, we leverage both media together efficiently for 3D pose estimation. We have shown that our proposed methodologies need not any synchronized 3D-2D pose-image pairs to retrieve and reconstruct the final 3D poses, and are flexible enough to capture motion in any studio-like indoor environment or outdoor natural environment. In first part of the dissertation, we propose model based approaches for full body human motion reconstruction from the video input by employing just 2D joint positions of the four end effectors and the head. We resolve the 3D-2D pose-image cross model correspondence by developing an intermediate container the knowledge base through the motion capture data which contains information about how people move. It includes the 3D normalized pose space and the corresponding synchronized 2D normalized pose space created by utilizing a number of virtual cameras. We first detect and track the features of these five joints from the input motion sequences using SURF, MSER and colorMSER feature detectors, which vote for the possible 2D locations for these joints in the video. The extraction of suitable feature sets from both, the input control signals and the motion capture data, enables us to retrieve the closest instances from the motion capture dataset through employing the fast searching and retrieval techniques. We develop a graphical structure online lazy neighbourhood graph in order to make the similarity search more accurate and robust by deploying the temporal coherence of the input control signals. The retrieved prior poses are exploited further in order to stabilize the feature detection and tracking process. Finally, the 3D motion sequences are reconstructed by a non-linear optimizer that takes into account multiple energy terms. We evaluate our approaches with a series of experiment scenarios designed in terms of performing actors, camera viewpoints and the noisy inputs. Only a little preprocessing is needed by our methods and the reconstruction processes run close to real time. The second part of the dissertation is dedicated to 3D human pose estimation from a monocular single image. First, we propose an efficient 3D pose retrieval strategy which leads towards a novel data driven approach to reconstruct a 3D human pose from a monocular still image. We design and devise multiple feature sets for global similarity search. At runtime, we search for the similar poses from a motion capture dataset in a definite feature space made up of specific joints. We introduce two-fold method for camera estimation, where we exploit the view directions at which we perform sampling of the MoCap dataset as well as the MoCap priors to minimize the projection error. We also benefit from the MoCap priors and the joints' weights in order to learn a low-dimensional local 3D pose model which is constrained further by multiple energies to infer the final 3D human pose. We thoroughly evaluate our approach on synthetically generated examples, the real internet images and the hand-drawn sketches. We achieve state-of-the-arts results when the test and MoCap data are from the same dataset and obtain competitive results when the motion capture data is taken from a different dataset. Second, we propose a dual source approach for 3D pose estimation from a single RGB image. One major challenge for 3D pose estimation from a single RGB image is the acquisition of sufficient training data. In particular, collecting large amounts of training data that contain unconstrained images and are annotated with accurate 3D poses is infeasible. We therefore propose to use two independent training sources. The first source consists of images with annotated 2D poses and the second source consists of accurate 3D motion capture data. To integrate both sources, we propose a dual-source approach that combines 2D pose estimation with efficient and robust 3D pose retrieval. In our experiments, we show that our approach achieves state-of-the-art results and is even competitive when the skeleton structures of the two sources differ substantially. In the last part of the dissertation, we focus on how the different techniques, developed for the human motion capturing, retrieval and reconstruction can be adapted to handle the quadruped motion capture data and which new applications may appear. We discuss some particularities which must be considered during capturing the large animal motions. For retrieval, we derive the suitable feature sets in order to perform fast searches into the MoCap dataset for similar motion segments. At the end, we present a data-driven approach to reconstruct the quadruped motions from the video input data

Efficient hand orientation and pose estimation for uncalibrated cameras

We proposed a staged probabilistic regression method that is capable of learning well from a number of variations within a dataset. The proposed method is based on multi layered Random Forest, where the first layer consisted of a single marginalization weights regressor and second layer contained an ensemble of expert learners. The expert learners are trained in stages, where each stage involved training and adding an expert learner to the intermediate model. After every stage, the intermediate model was evaluated to reveal a latent variable space defining a subset that the model had difficulty in learning from. This subset was used to train the next expert regressor. The posterior probabilities for each training sample were extracted from each expert regressors. These posterior probabilities were then used along with a Kullback-Leibler divergence-based optimization method to estimate the marginalization weights for each regressor. A marginalization weights regressor was trained using CDF and the estimated marginalization weights. We showed the extension of our work for simultaneous hand orientation and pose inference. The proposed method outperformed the state-of-the-art for marginalization of multi-layered Random Forest and hand orientation inference. Furthermore, we show that a method which simultaneously learns from hand orientation and pose outperforms pose classification as it is able to better understand the variations in pose induced due to viewpoint changes