Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Related Works <s> Robust detection of vehicle occupant posture is necessary for intelligent airbag deployment. This paper presents a vision-based method of estimating the size, posture and pose of the occupant. Utilizing raw reflectance and stereo disparity images, this algorithm presents a mixed-mode approach to finding occupant features. Extensive experiments show the viability of this mixed-mode method in identifying the occupant's head location and suggest the feasibility of extending the analysis system to include detection of features such as occupant arms and foreign objects. <s> BIB001 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Related Works <s> Vision-based human pose estimation is useful in pervasive computing. In this paper, we proposed an example-based approach to human pose estimation from monocular image sequences. We use human motion capture data to synthesize a pose example database with each pose's 3D information known. Firstly, we use shape context to describe the human silhouette detected from video frames, and get candidates from the pose database by silhouette matching; Secondly, we build probability and statistical model of motion, and carry out pose estimation from these candidates; Finally, Kernel Regression is used to smooth the motion. The proposed method could effectively analyze 3D pose from video and solve the orientation ambiguities problem, also it is invariant to view points. The effectiveness of this method is verified on videos of walking, running and jumping. <s> BIB002 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Related Works <s> Vision-based human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as visual surveillance, video retrieval and human-computer interaction. The task is challenging due to variations in motion performance, recording settings and inter-personal differences. In this survey, we explicitly address these challenges. We provide a detailed overview of current advances in the field. Image representations and the subsequent classification process are discussed separately to focus on the novelties of recent research. Moreover, we discuss limitations of the state of the art and outline promising directions of research. <s> BIB003 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Related Works <s> Action recognition has become a very important topic in computer vision, with many fundamental applications, in robotics, video surveillance, human-computer interaction, and multimedia retrieval among others and a large variety of approaches have been described. The purpose of this survey is to give an overview and categorization of the approaches used. We concentrate on approaches that aim on classification of full-body motions, such as kicking, punching, and waving, and we categorize them according to how they represent the spatial and temporal structure of actions; how they segment actions from an input stream of visual data; and how they learn a view-invariant representation of actions. <s> BIB004 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Related Works <s> Action recognition and pose estimation are two closely related topics in understanding human body movements; information from one task can be leveraged to assist the other, yet the two are often treated separately. We present here a framework for coupled action recognition and pose estimation by formulating pose estimation as an optimization over a set of action-specific manifolds. The framework allows for integration of a 2D appearance-based action recognition system as a prior for 3D pose estimation and for refinement of the action labels using relational pose features based on the extracted 3D poses. Our experiments show that our pose estimation system is able to estimate body poses with high degrees of freedom using very few particles and can achieve state-of-the-art results on the HumanEva-II benchmark. We also thoroughly investigate the impact of pose estimation and action recognition accuracy on each other on the challenging TUM kitchen dataset. We demonstrate not only the feasibility of using extracted 3D poses for action recognition, but also improved performance in comparison to action recognition using low-level appearance features. <s> BIB005 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Related Works <s> We present a novel method for human action recognition (HAR) based on estimated poses from image sequences. We use 3D human pose data as additional information and propose a compact human pose representation, called a weak pose, in a low-dimensional space while still keeping the most discriminative information for a given pose. With predicted poses from image features, we map the problem from image feature space to pose space, where a Bag of Poses (BOP) model is learned for the final goal of HAR. The BOP model is a modified version of the classical bag of words pipeline by building the vocabulary based on the most representative weak poses for a given action. Compared with the standard k-means clustering, our vocabulary selection criteria is proven to be more efficient and robust against the inherent challenges of action recognition. Moreover, since for action recognition the ordering of the poses is discriminative, the BOP model incorporates temporal information: in essence, groups of consecutive poses are considered together when computing the vocabulary and assignment. We tested our method on two well-known datasets: HumanEva and IXMAS, to demonstrate that weak poses aid to improve action recognition accuracies. The proposed method is scene-independent and is comparable with the state-of-art method. <s> BIB006 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Related Works <s> Analysis of human behaviour through visual information has been a highly active research topic in the computer vision community. This was previously achieved via images from a conventional camera, however recently depth sensors have made a new type of data available. This survey starts by explaining the advantages of depth imagery, then describes the new sensors that are available to obtain it. In particular, the Microsoft Kinect has made high-resolution real-time depth cheaply available. The main published research on the use of depth imagery for analysing human activity is reviewed. Much of the existing work focuses on body part detection and pose estimation. A growing research area addresses the recognition of human actions. The publicly available datasets that include depth imagery are listed, as are the software libraries that can acquire it from a sensor. This survey concludes by summarising the current state of work on this topic, and pointing out promising future research directions. For both researchers and practitioners who are familiar with this topic and those who are new to this field, the review will aid in the selection, and development, of algorithms using depth data. <s> BIB007 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Related Works <s> Human Pose Recovery has been studied in the field of Computer Vision for the last 40 years. Several approaches have been reported, and significant improvements have been obtained in both data representation and model design. However, the problem of Human Pose Recovery in uncontrolled environments is far from being solved. In this paper, we define a general taxonomy to group model based approaches for Human Pose Recovery, which is composed of five main modules: appearance, viewpoint, spatial relations, temporal consistence, and behavior. Subsequently, a methodological comparison is performed following the proposed taxonomy, evaluating current SoA approaches in the aforementioned five group categories. As a result of this comparison, we discuss the main advantages and drawbacks of the reviewed literature. <s> BIB008 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Related Works <s> Summarization of methods on human pose estimation in recent years.Conclusion of the traditional human pose estimation methods.Illustrated based on a two-stage framework.Comprehensive comparisons are given based on the open source methods. Estimating human pose from videos and image sequences is not only an important computer vision problem, but also plays very critical role in many real-world applications. Main challenges for human pose estimation are variation of body poses, complicated background and depth ambiguities. To solve these problems, considerable research efforts have been devoted to the related fields. In this survey, we focus our attention on the recent advances in vision-based human pose estimation. We first present a general framework of human pose estimation, and then go through the latest technical progress on each stage. Finally, we discuss the limitations of the existing approaches and foresee the future directions to be explored. <s> BIB009 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Related Works <s> Action recognition and pose estimation from video are closely related tasks for understanding human motion, most methods, however, learn separate models and combine them sequentially. In this paper, we propose a framework to integrate training and testing of the two tasks. A spatial-temporal And-Or graph model is introduced to represent action at three scales. Specifically the action is decomposed into poses which are further divided to mid-level ST-parts and then parts. The hierarchical structure of our model captures the geometric and appearance variations of pose at each frame and lateral connections between ST-parts at adjacent frames capture the action-specific motion information. The model parameters for three scales are learned discriminatively, and action labels and poses are efficiently inferred by dynamic programming. Experiments demonstrate that our approach achieves state-of-art accuracy in action recognition while also improving pose estimation. <s> BIB010
Several surveys of human pose estimation can be found in literature. The authors of BIB001   BIB002 give surveys of vision-based human pose estimation, but these works were conducted before 2009. A more recent comprehensive survey is from Liu et al. BIB009 . This survey studied human pose estimation from several types of input images under various types of camera settings (both single-view and multiple-view), and includes 104 references. In our survey, more than 300 references are included, and these works concentrate on a specific type of input: monocular images. Other recent surveys were carried out on specific methodologies.For example, the survey from Lepetit et al.  and the survey from Perez-Sala et al. BIB008 both study model-based approaches, which employ human body knowledge such as the human body's appearance and structure for the enhancement of human pose estimation. There are also surveys dedicated to human motion analysis where motion information is prerequisite     . An area that is closely related to human pose estimation is action recognition. Although algorithms and techniques used in human action recognition are different from those used in human pose estimation, recognition results of these two are sometimes combined within a framework to boost the performance of a single task  BIB005 BIB010 BIB006 . Surveys on action recognition include BIB003 BIB004 [29] BIB007 .