In this paper we study the problem of estimating innercyclic time intervals within repetitive motion sequences of top-class swimmers in a swimming channel. Interval limits are given by temporal occurrences of key-poses, i.e. distinctive postures of the body. A key-pose is defined by means of only one or two specific features of the complete posture. It is often difficult to detect such subtle features directly. We therefore propose the following method: Given that we observe the swimmer from the side, we build a pictorial structure of poselets to robustly identify random support poses within the regular motion of a swimmer. We formulate a maximum likelihood model which predicts a key-pose given the occurrences of multiple support poses within one stroke. The maximum likelihood can be extended with prior knowledge about the temporal location of a key-pose in order to improve the prediction recall. We experimentally show that our models reliably and robustly detect key-poses with a high precision and that their performance can be improved by extending the framework with additional camera views.
Part based models have played a huge role in the fields of object detection and (human) pose estimation within the last years. Based on the fundamental work Fischler and Elschlager @cite_12 , these models represent an object through multiple parts which are connected via deformation terms, often visualized as springs, allowing for matching them in a flexible configuration. Various manifestations of this basic notion have been developed through the years, kicked of by @cite_21 with their deformable part models for object detection. Different refinements have been proposed specifically for human pose estimation, e.g. by enriching a model with additional parts to compensate for the flexibility of the human body @cite_23 or by allowing rotation of parts @cite_16 .
@cite_21 @cite_23 @cite_12 @cite_16
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In this paper we present a computationally efficient framework for part-based modeling and recognition of objects. Our work is motivated by the pictorial structure models introduced by Fischler and Elschlager. The basic idea is to represent an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. We address the problem of using pictorial structure models to find instances of an object in an image as well as the problem of learning an object model from training examples, presenting efficient algorithms in both cases. We demonstrate the techniques by learning models that represent faces and human bodies and using the resulting models to locate the corresponding objects in novel images. Detecting objects in cluttered scenes and estimating articulated human body parts are two challenging problems in computer vision. The difficulty is particularly pronounced in activities involving human-object interactions (e.g. playing tennis), where the relevant object tends to be small or only partially visible, and the human body parts are often self-occluded. We observe, however, that objects and human poses can serve as mutual context to each other – recognizing one facilitates the recognition of the other. In this paper we propose a new random field model to encode the mutual context of objects and human poses in human-object interaction activities. We then cast the model learning task as a structure learning problem, of which the structural connectivity between the object, the overall human pose, and different body parts are estimated through a structure search approach, and the parameters of the model are estimated by a new max-margin algorithm. On a sports data set of six classes of human-object interactions [12], we show that our mutual context model significantly outperforms state-of-the-art in detecting very difficult objects and human poses. 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. This paper explores the localization of pre-defined semantic object parts, which is much more challenging than traditional object detection and very important for applications such as face recognition, HCI and fine-grained object recognition. To address this problem, we make two critical improvements over the widely used deformable part model (DPM). The first is that we use appearance based shape regression to globally estimate the anchor location of each part and then locally refine each part according to the estimated anchor location under the constraint of DPM. The DPM with shape regression (SR-DPM) is more flexible than the traditional DPM by relaxing the fixed anchor location of each part. It enjoys the efficient dynamic programming inference as traditional DPM and can be discriminatively trained via a coordinate descent procedure. The second is that we propose to stack multiple SR-DPMs, where each layer uses the output of previous SR-DPM as the input to progressively refine the result. It provides an analogy to deep neural network while benefiting from hand-crafted feature and model. The proposed methods are applied to human pose estimation, face alignment and general object part localization tasks and achieve state-of-the-art performance. Although the human visual system is surprisingly robust to extreme distortion when recognizing objects, most evaluations of computer object detection methods focus only on robustness to natural form deformations such as people’s pose changes. To determine whether algorithms truly mirror the flexibility of human vision, they must be compared against human vision at its limits. For example, in Cubist abstract art, painted objects are distorted by object fragmentation and part-reorganization, sometimes to the point that human vision often fails to recognize them. In this paper, we evaluate existing object detection methods on these abstract renditions of objects, comparing human annotators to four state-of-the-art object detectors on a corpus of Picasso paintings. Our results demonstrate that while human perception significantly outperforms current methods, human perception and part-based models exhibit a similarly graceful degradation in object detection performance as the objects become increasingly abstract and fragmented, corroborating the theory of part-based object representation in the brain. Detecting objects in cluttered scenes and estimating articulated human body parts from 2D images are two challenging problems in computer vision. The difficulty is particularly pronounced in activities involving human-object interactions (e.g., playing tennis), where the relevant objects tend to be small or only partially visible and the human body parts are often self-occluded. We observe, however, that objects and human poses can serve as mutual context to each other-recognizing one facilitates the recognition of the other. In this paper, we propose a mutual context model to jointly model objects and human poses in human-object interaction activities. In our approach, object detection provides a strong prior for better human pose estimation, while human pose estimation improves the accuracy of detecting the objects that interact with the human. On a six-class sports data set and a 24-class people interacting with musical instruments data set, we show that our mutual context model outperforms state of the art in detecting very difficult objects and estimating human poses, as well as classifying human-object interaction activities. We propose a new 3D model of the human body that is both realistic and part-based. The body is represented by a graphical model in which nodes of the graph correspond to body parts that can independently translate and rotate in 3D and deform to represent different body shapes and to capture pose-dependent shape variations. Pairwise potentials define a “stitching cost” for pulling the limbs apart, giving rise to the stitched puppet (SP) model. Unlike existing realistic 3D body models, the distributed representation facilitates inference by allowing the model to more effectively explore the space of poses, much like existing 2D pictorial structures models. We infer pose and body shape using a form of particle-based max-product belief propagation. This gives SP the realism of recent 3D body models with the computational advantages of part-based models. We apply SP to two challenging problems involving estimating human shape and pose from 3D data. The first is the FAUST mesh alignment challenge, where ours is the first method to successfully align all 3D meshes with no pose prior. The second involves estimating pose and shape from crude visual hull representations of complex body movements. Recently, Deep Convolutional Neural Networks (DCNNs) have been applied to the task of human pose estimation, and have shown its potential of learning better feature representations and capturing contextual relationships. However, it is difficult to incorporate domain prior knowledge such as geometric relationships among body parts into DCNNs. In addition, training DCNN-based body part detectors without consideration of global body joint consistency introduces ambiguities, which increases the complexity of training. In this paper, we propose a novel end-to-end framework for human pose estimation that combines DCNNs with the expressive deformable mixture of parts. We explicitly incorporate domain prior knowledge into the framework, which greatly regularizes the learning process and enables the flexibility of our framework for loopy models or tree-structured models. The effectiveness of jointly learning a DCNN with a deformable mixture of parts model is evaluated through intensive experiments on several widely used benchmarks. The proposed approach significantly improves the performance compared with state-of-the-art approaches, especially on benchmarks with challenging articulations. Human pose estimation and semantic part segmentation are two complementary tasks in computer vision. In this paper, we propose to solve the two tasks jointly for natural multi-person images, in which the estimated pose provides object-level shape prior to regularize part segments while the part-level segments constrain the variation of pose locations. Specifically, we first train two fully convolutional neural networks (FCNs), namely Pose FCN and Part FCN, to provide initial estimation of pose joint potential and semantic part potential. Then, to refine pose joint location, the two types of potentials are fused with a fully-connected conditional random field (FCRF), where a novel segment-joint smoothness term is used to encourage semantic and spatial consistency between parts and joints. To refine part segments, the refined pose and the original part potential are integrated through a Part FCN, where the skeleton feature from pose serves as additional regularization cues for part segments. Finally, to reduce the complexity of the FCRF, we induce human detection boxes and infer the graph inside each box, making the inference forty times faster. Since there's no dataset that contains both part segments and pose labels, we extend the PASCAL VOC part dataset with human pose joints and perform extensive experiments to compare our method against several most recent strategies. We show that on this dataset our algorithm surpasses competing methods by a large margin in both tasks. Detecting objects becomes difficult when we need to deal with large shape deformation, occlusion and low resolution. We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examples of highly deformable objects), ii) describe them in terms of body parts, and iii) detect them when their body parts are hard to detect (e.g., animals depicted at low resolution). We represent the holistic object and body parts separately and use a fully connected model to arrange templates for the holistic object and body parts. Our model automatically decouples the holistic object or body parts from the model when they are hard to detect. This enables us to represent a large number of holistic object and body part combinations to better deal with different "detectability" patterns caused by deformations, occlusion and or low resolution. We apply our method to the six animal categories in the PASCAL VOC dataset and show that our method significantly improves state-of-the-art (by 4.1 AP) and provides a richer representation for objects. During training we use annotations for body parts (e.g., head, torso, etc.), making use of a new dataset of fully annotated object parts for PASCAL VOC 2010, which provides a mask for each part. Simple tree models for articulated objects prevails in the last decade. However, it is also believed that these simple tree models are not capable of capturing large variations in many scenarios, such as human pose estimation. This paper attempts to address three questions: 1) are simple tree models sufficient? more specifically, 2) how to use tree models effectively in human pose estimation? and 3) how shall we use combined parts together with single parts efficiently? Assuming we have a set of single parts and combined parts, and the goal is to estimate a joint distribution of their locations. We surprisingly find that no latent variables are introduced in the Leeds Sport Dataset (LSP) during learning latent trees for deformable model, which aims at approximating the joint distributions of body part locations using minimal tree structure. This suggests one can straightforwardly use a mixed representation of single and combined parts to approximate their joint distribution in a simple tree model. As such, one only needs to build Visual Categories of the combined parts, and then perform inference on the learned latent tree. Our method outperformed the state of the art on the LSP, both in the scenarios when the training images are from the same dataset and from the PARSE dataset. Experiments on animal images from the VOC challenge further support our findings. We describe a method for human pose estimation in static images based on a novel representation of part models. Notably, we do not use articulated limb parts, but rather capture orientation with a mixture of templates for each part. We describe a general, flexible mixture model for capturing contextual co-occurrence relations between parts, augmenting standard spring models that encode spatial relations. We show that such relations can capture notions of local rigidity. When co-occurrence and spatial relations are tree-structured, our model can be efficiently optimized with dynamic programming. We present experimental results on standard benchmarks for pose estimation that indicate our approach is the state-of-the-art system for pose estimation, outperforming past work by 50 while being orders of magnitude faster. We propose a new learning-based method for estimating 2D human pose from a single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN). Recently, many methods have been developed to estimate human pose by using pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective. In this paper, we propose to integrate both the local (body) part appearance and the holistic view of each local part for more accurate human pose estimation. Specifically, the proposed DS-CNN takes a set of image patches (category-independent object proposals for training and multi-scale sliding windows for testing) as the input and then learns the appearance of each local part by considering their holistic views in the full body. Using DS-CNN, we achieve both joint detection, which determines whether an image patch contains a body joint, and joint localization, which finds the exact location of the joint in the image patch. Finally, we develop an algorithm to combine these joint detection localization results from all the image patches for estimating the human pose. The experimental results show the effectiveness of the proposed method by comparing to the state-of-the-art human-pose estimation methods based on pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective. We describe a method for articulated human detection and human pose estimation in static images based on a new representation of deformable part models. Rather than modeling articulation using a family of warped (rotated and foreshortened) templates, we use a mixture of small, nonoriented parts. We describe a general, flexible mixture model that jointly captures spatial relations between part locations and co-occurrence relations between part mixtures, augmenting standard pictorial structure models that encode just spatial relations. Our models have several notable properties: 1) They efficiently model articulation by sharing computation across similar warps, 2) they efficiently model an exponentially large set of global mixtures through composition of local mixtures, and 3) they capture the dependency of global geometry on local appearance (parts look different at different locations). When relations are tree structured, our models can be efficiently optimized with dynamic programming. We learn all parameters, including local appearances, spatial relations, and co-occurrence relations (which encode local rigidity) with a structured SVM solver. Because our model is efficient enough to be used as a detector that searches over scales and image locations, we introduce novel criteria for evaluating pose estimation and human detection, both separately and jointly. We show that currently used evaluation criteria may conflate these two issues. Most previous approaches model limbs with rigid and articulated templates that are trained independently of each other, while we present an extensive diagnostic evaluation that suggests that flexible structure and joint training are crucial for strong performance. We present experimental results on standard benchmarks that suggest our approach is the state-of-the-art system for pose estimation, improving past work on the challenging Parse and Buffy datasets while being orders of magnitude faster. The performance of part-based object detectors generally degrades for highly flexible objects. The limited topological structure of models and pre-specified part shapes are two main factors preventing these detectors from fully capturing large deformations. To better capture the deformations, we propose a novel approach to integrate the detections from a family of part-based detectors with patches of objects that have irregular shape. This integration is formulated as MAP inference in a Conditional Random Field (CRF). The energy function defined over the CRF takes into account the information provided by an object patch classifier and the object detector, and the goal is to augment the partial detections with missing patches, and also to refine the detections that include background clutter. The proposed method is evaluated on the object detection task of PASCAL VOC. Our experimental results show significant improvement over a base part-based detector (which is among the current state-of-the-art methods) especially for the deformable object classes. The objective of this paper is to estimate 2D human pose as a spatial configuration of body parts in TV and movie video shots. Such video material is uncontrolled and extremely challenging. We propose an approach that progressively reduces the search space for body parts, to greatly improve the chances that pose estimation will succeed. This involves two contributions: (i) a generic detector using a weak model of pose to substantially reduce the full pose search space; and (ii) employing 'grabcut' initialized on detected regions proposed by the weak model, to further prune the search space. Moreover, we also propose (Hi) an integrated spatio- temporal model covering multiple frames to refine pose estimates from individual frames, with inference using belief propagation. The method is fully automatic and self-initializing, and explains the spatio-temporal volume covered by a person moving in a shot, by soft-labeling every pixel as belonging to a particular body part or to the background. We demonstrate upper-body pose estimation by an extensive evaluation over 70000 frames from four episodes of the TV series Buffy the vampire slayer, and present an application to full- body action recognition on the Weizmann dataset. Recognizing humans, estimating their pose and segmenting their body parts are key to high-level image understanding. Because humans are highly articulated, the range of deformations they undergo makes this task extremely challenging. Previous methods have focused largely on heuristics or pairwise part models in approaching this problem. We propose a bottom-up parsing of increasingly more complete partial body masks guided by a parse tree. At each level of the parsing process, we evaluate the partial body masks directly via shape matching with exemplars, without regard to how the parses are formed. The body is evaluated as a whole, not the sum of its constituent parses, unlike previous approaches. Multiple image segmentations are included at each of the levels of the parsing, to augment existing parses or to introduce ones. Our method yields both a pose estimate as well as a segmentation of the human. We demonstrate competitive results on this challenging task with relatively few training examples on a dataset of baseball players with wide pose variation. Our method is comparatively simple and could be easily extended to other objects. In this paper we consider people detection and articulated pose estimation, two closely related and challenging problems in computer vision. Conceptually, both of these problems can be addressed within the pictorial structures framework (Felzenszwalb and Huttenlocher in Int. J. Comput. Vis. 61(1):55---79, 2005; Fischler and Elschlager in IEEE Trans. Comput. C-22(1):67---92, 1973), even though previous approaches have not shown such generality. A principal difficulty for such a general approach is to model the appearance of body parts. The model has to be discriminative enough to enable reliable detection in cluttered scenes and general enough to capture highly variable appearance. Therefore, as the first important component of our approach, we propose a discriminative appearance model based on densely sampled local descriptors and AdaBoost classifiers. Secondly, we interpret the normalized margin of each classifier as likelihood in a generative model and compute marginal posteriors for each part using belief propagation. Thirdly, non-Gaussian relationships between parts are represented as Gaussians in the coordinate system of the joint between the parts. Additionally, in order to cope with shortcomings of tree-based pictorial structures models, we augment our model with additional repulsive factors in order to discourage overcounting of image evidence. We demonstrate that the combination of these components within the pictorial structures framework results in a generic model that yields state-of-the-art performance for several datasets on a variety of tasks: people detection, upper body pose estimation, and full body pose estimation. This paper provides the first comprehensive analysis of Uber's driver-partners, based on both survey data and anonymized, aggregated administrative data. Uber has grown at an exponential rate over the last few years, and drivers who partner with Uber appear to be attracted to the platform in large part because of the flexibility it offers, the level of compensation, and the fact that earnings per hour do not vary much with hours worked, which facilitates part-time and variable hours. Uber's driver-partners are more similar in terms of their age and education to the general workforce than to taxi drivers and chauffeurs. Uber may serve as a bridge for many seeking other employment opportunities, and it may attract well-qualified individuals because, with Uber's star rating system, driver-partners' reputations are explicitly shared with potential customers. Most of Uber's driver-partners had full- or part-time employment prior to joining Uber, and many continued in those positions after starting to drive with the Uber platform, which makes the flexibility to set their own hours all the more valuable. Uber's driver-partners also often cited the desire to smooth fluctuations in their income as a reason for partnering with Uber. Many object classes, including human faces, can be modeled as a set of characteristic parts arranged in a variable spatial configuration. We introduce a simplified model of a deformable object class and derive the optimal detector for this model. However, the optimal detector is not realizable except under special circumstances (independent part positions). A cousin of the optimal detector is developed which uses “soft” part detectors with a probabilistic description of the spatial arrangement of the parts. Spatial arrangements are modeled probabilistically using shape statistics to achieve invariance to translation, rotation, and scaling. Improved recognition performance over methods based on “hard” part detectors is demonstrated for the problem of face detection in cluttered scenes.