Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> High-Level Features <s> A model of human appearance is presented for efficient pose estimation from real-world images. In common with related approaches, a high-level model defines a space of configurations which can be associated with image measurements and thus scored. A search is performed to identify good configuration(s). Such an approach is challenging because the configuration space is high dimensional, the search is global, and the appearance of humans in images is complex due to background clutter, shape uncertainty and texture. <s> BIB001 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> High-Level Features <s> Bourdev and Malik (ICCV 09) introduced a new notion of parts, poselets, constructed to be tightly clustered both in the configuration space of keypoints, as well as in the appearance space of image patches. In this paper we develop a new algorithm for detecting people using poselets. Unlike that work which used 3D annotations of keypoints, we use only 2D annotations which are much easier for naive human annotators. The main algorithmic contribution is in how we use the pattern of poselet activations. Individual poselet activations are noisy, but considering the spatial context of each can provide vital disambiguating information, just as object detection can be improved by considering the detection scores of nearby objects in the scene. This can be done by training a two-layer feed-forward network with weights set using a max margin technique. The refined poselet activations are then clustered into mutually consistent hypotheses where consistency is based on empirically determined spatial keypoint distributions. Finally, bounding boxes are predicted for each person hypothesis and shape masks are aligned to edges in the image to provide a segmentation. To the best of our knowledge, the resulting system is the current best performer on the task of people detection and segmentation with an average precision of 47.8% and 40.5% respectively on PASCAL VOC 2009. <s> BIB002 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> High-Level Features <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> BIB003 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> High-Level Features <s> A k-poselet is a deformable part model (DPM) with k parts, where each of the parts is a poselet, aligned to a specific configuration of keypoints based on ground-truth annotations. A separate template is used to learn the appearance of each part. The parts are allowed to move with respect to each other with a deformation cost that is learned at training time. This model is richer than both the traditional version of poselets and DPMs. It enables a unified approach to person detection and keypoint prediction which, barring contemporaneous approaches based on CNN features, achieves state-of-the-art keypoint prediction while maintaining competitive detection performance. <s> BIB004 </s> Human Pose Estimation from Monocular Images: A Comprehensive Survey <s> High-Level Features <s> Human pose detection is the foundation for many applications, particularly those using gestures as part of a natural user interface. We introduce a novel task-based control method for human pose detection, encoding specialist knowledge in a descriptive abstraction for application by non-experts, such as developers, artists and students. The abstraction hides the details of a set of algorithms which specialise either in different estimations of pose (e.g. articulated, body part) or under different conditions (e.g. occlusion, clutter). Users describe the conditions of their problem, which is used to select the most suitable algorithm (and automatically set up the parameters). The task-based control is evaluated with images described using the abstraction. Expected outcomes are compared to results and demonstrate that describing the conditions is sufficient to allow the abstraction to produce the required result. <s> BIB005
Several descriptors have high-level characteristics, such as body part patches, geometry descriptors, or context features. Body part patches assume any of the spaced orientation, and they can have any position inside the patch. They are more general descriptors compared to body parts, which are confined within a body limb, between body joints, or within the vicinity of a body joint. The combined body parts, as a geometry descriptor, contain semantic relations among single parts BIB001 BIB002 BIB004 , usually encoded as putting two sets of features together, including body parts' location and orientation BIB003 . Context, on the other hand, captures spatial or temporal correlations, and can represent task-specific features BIB005 . High-level features encode semantic co-occurrence between composing units. Compared with mid-level features, which are a spatial or temporal encoding in a predefined pattern, high-level features mine correlations from training data and let data speak for itself.