Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Planar Model <s> There has been much effort invested in increasing the robustness of human body tracking by incorporating motion models. Most approaches are probabilistic in nature and seek to avoid becoming trapped into local minima by considering multiple hypotheses, which typically requires exponentially large amounts of computation as the number of degrees of freedom increases. <s> BIB001 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Planar Model <s> We define a new “contour person” model of the human body that has the expressive power of a detailed 3D model and the computational benefits of a simple 2D part-based model. The contour person (CP) model is learned from a 3D SCAPE model of the human body that captures natural shape and pose variations; the projected contours of this model, along with their segmentation into parts forms the training set. The CP model factors deformations of the body into three components: shape variation, viewpoint change and part rotation. This latter model also incorporates a learned non-rigid deformation model. The result is a 2D articulated model that is compact to represent, simple to compute with and more expressive than previous models. We demonstrate the value of such a model in 2D pose estimation and segmentation. Given an initial pose from a standard pictorial-structures method, we refine the pose and shape using an objective function that segments the scene into foreground and background regions. The result is a parametric, human-specific, image segmentation. <s> BIB002 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> Planar Model <s> We propose a novel method to detect human poses in videos by concurrently optimizing body part matching and object segmentation. With a single exemplar image, the proposed method detects the poses of a specific human subject in long video sequences. Matching and segmentation support each other and therefore the simultaneous optimization enables more reliable results. However, efficient concurrent optimization is a great challenge due to its huge search space. We propose an efficient linear method that solves the problem. In this method, the optimal body part matching conforms to local appearances and a human body plan, and the body part configuration is consistent with the object foreground estimated by simultaneous superpixel labeling. Our experiments on a variety of videos show that the proposed method is efficient and more reliable than previous locally constrained approaches. <s> BIB003
Other than capturing the connecting relations between body parts, planar models are also capable of learning appearance. Various means are used to learn the shape and appearance of human body parts. One example is Active Shape Models (ASMs). ASMs are used to represent the full human body and capture the statistics of contour deformations from a mean shape using principal component analysis (PCA)  BIB002  BIB001 . Another example is the cardboard model (shown in Figure 10b ), composed of information about object foreground colors and body part rectangular shapes. The cardboard model usually has a torso and eight half limbs, each body part's appearance is represented by the average RGB color, and the foreground color histogram is also stored. For example, the authors of BIB003 used the cardboard model for human pose estimation.