Learning Generative Models for Multi-Activity Body Pose Estimation

We present a method to simultaneously estimate 3D body pose and action categories from monocular video sequences. Our approach learns a generative model of the relationship of body pose and image appearance using a sparse kernel regressor. Body poses are modelled on a low-dimensional manifold obtained by Locally Linear Embedding dimensionality reduction. In addition, we learn a prior model of likely body poses and a dynamical model in this pose manifold. Sparse kernel regressors capture the nonlinearities of this mapping efficiently. Within a Recursive Bayesian Sampling framework, the potentially multimodal posterior probability distributions can then be inferred. An activity-switching mechanism based on learned transfer functions allows for inference of the performed activity class, along with the estimation of body pose and 2D image location of the subject. Using a rough foreground segmentation, we compare Binary PCA and distance transforms to encode the appearance. As a postprocessing step, the globally optimal trajectory through the entire sequence is estimated, yielding a single pose estimate per frame that is consistent throughout the sequence. We evaluate the algorithm on challenging sequences with subjects that are alternating between running and walking movements. Our experiments show how the dynamical model helps to track through poorly segmented low-resolution image sequences where tracking otherwise fails, while at the same time reliably classifying the activity type.     

A model-based approach for estimating human 3D poses in static images

Estimating human body poses in static images is important for many image understanding applications including semantic content extraction and image database query and retrieval. This problem is challenging due to the presence of clutter in the image, ambiguities in image observation, unknown human image boundary, and high-dimensional state space due to the complex articulated structure of the human body. Human pose estimation can be made more robust by integrating the detection of body components such as face and limbs, with the highly constrained structure of the articulated body. In this paper, a data-driven approach based on Markov chain Monte Carlo (DD-MCMC) is used, where component detection results generate state proposals for 3D pose estimation. To translate these observations into pose hypotheses, we introduce the use of "proposal maps," an efficient way of consolidating the evidence and generating 3D pose candidates during the MCMC search. Experimental results on a set of test images show that the method is able to estimate the human pose in static images of real scenes.     

Improving articulated hand pose detection for static finger sign recognition in RGB-D images

Multimedia Tools and Applications - With the emergence of consumer RGB-D sensors, discriminative modeling has been shown to perform well in estimating human body pose. However, articulated hand...     

利用改进生成对抗网络进行人体姿态识别

针对人体模型中某些重要关节点准确定位的问题,提出了一种新型深度卷积生成对抗网络以进行静态图像中人体姿态的估计的方法。该方法采用了深度卷积的堆叠沙漏网络来准确提取图像上关键关节点的位置,该网络的生成和辨别部分被设计用于编码第一层次结构（亲本）与第二层次结构（子本）中的空间关系,并且展示了人体部位的空间层次。生成器和判别器在网络中被设计为两部分,并按照顺序连接在一起用来编码外观可能的关系,同时为人体部位存在的可能性以及身体的每个部分与其亲本部分之间的关系进行编码。在静态图像中,可以较准确地识别人体模型关键节点以及大致人体姿态。该方法在不同的数据集上进行了实验,在大部分情况下,提出的方法获得的结果优于其他几种对比方法。     

Generative tracking of 3D human motion in latent space by sequential clonal selection algorithm

High dimensional pose state space is the main challenge in articulated human pose tracking which makes pose analysis computationally expensive or even infeasible. In this paper, we propose a novel generative approach in the framework of evolutionary computation, by which we try to widen the bottleneck with effective search strategy embedded in the extracted state subspace. Firstly, we use ISOMAP to learn the low-dimensional latent space of pose state in the aim of both reducing dimensionality and extracting the prior knowledge of human motion simultaneously. Then, we propose a manifold reconstruction method to establish smooth mappings between the latent space and original space, which enables us to perform pose analysis in the latent space. In the search strategy, we adopt a new evolutionary approach, clonal selection algorithm (CSA), for pose optimization. We design a CSA based method to estimate human pose from static image, which can be used for initialization of motion tracking. In order to make CSA suitable for motion tracking, we propose a sequential CSA (S-CSA) algorithm by incorporating the temporal continuity information into the traditional CSA. Actually, in a Bayesian inference view, the sequential CSA algorithm is in essence a multilayer importance sampling based particle filter. Our methods are demonstrated in different motion types and different image sequences. Experimental results show that our CSA based pose estimation method can achieve viewpoint invariant 3D pose reconstruction and the S-CSA based motion tracking method can achieve accurate and stable tracking of 3D human motion.     

3D Human model adaptation by frame selection and shape–texture optimization

We present a novel approach for 3D human body shape model adaptation to a sequence of multi-view images, given an initial shape model and initial pose sequence. In a first step, the most informative frames are determined by optimization of an objective function that maximizes a shape–texture likelihood function and a pose diversity criterion (i.e. the model surface area that lies close to the occluding contours), in the selected frames. Thereafter, a batch-mode optimization is performed of the underlying shape- and pose-parameters, by means of an objective function that includes both contour and texture cues over the selected multi-view frames.Using above approach, we implement automatic pose and shape estimation using a three-step procedure: first, we recover initial poses over a sequence using an initial (generic) body model. Both model and poses then serve as input to the above mentioned adaptation process. Finally, a more accurate pose recovery is obtained by means of the adapted model.We demonstrate the effectiveness of our frame selection, model adaptation and integrated pose and shape recovery procedure in experiments using both challenging outdoor data and the HumanEva data set.     

Learning a Deep Predictive Coding Network for a Semi-Supervised 3D-Hand Pose Estimation

In this paper we present a CNN based approach for a real time 3D-hand pose estimation from the depth sequence. Prior discriminative approaches have achieved remarkable success but are facing two main challenges: Firstly, the methods are fully supervised hence require large numbers of annotated training data to extract the dynamic information from a hand representation. Secondly, unreliable hand detectors based on strong assumptions or a weak detector which often fail in several situations like complex environment and multiple hands. In contrast to these methods, this paper presents an approach that can be considered as semi-supervised by performing predictive coding of image sequences of hand poses in order to capture latent features underlying a given image without supervision. The hand is modelled using a novel latent tree dependency model (LDTM) which transforms internal joint location to an explicit representation. Then the modeled hand topology is integrated with the pose estimator using data dependent method to jointly learn latent variables of the posterior pose appearance and the pose configuration respectively. Finally, an unsupervised error term which is a part of the recurrent architecture ensures smooth estimations of the final pose. Experiments on three challenging public datasets, ICVL, MSRA, and NYU demonstrate the significant performance of the proposed method which is comparable or better than state-of-the-art approaches.      

视频中多特征融合人体姿态跟踪

目的 目前已有的人体姿态跟踪算法的跟踪精度仍有待提高,特别是对灵活运动的手臂部位的跟踪。为提高人体姿态的跟踪精度,本文首次提出一种将视觉时空信息与深度学习网络相结合的人体姿态跟踪方法。方法 在人体姿态跟踪过程中,利用视频时间信息计算出人体目标区域的运动信息,使用运动信息对人体部位姿态模型在帧间传递;考虑到基于图像空间特征的方法对形态较为固定的人体部位如躯干和头部能够较好地检测,而对手臂的检测效果较差,构造并训练一种轻量级的深度学习网络,用于生成人体手臂部位的附加候选样本;利用深度学习网络生成手臂特征一致性概率图,与视频空间信息结合计算得到最优部位姿态,并将各部位重组为完整人体姿态跟踪结果。结果 使用两个具有挑战性的人体姿态跟踪数据集VideoPose2.0和YouTubePose对本文算法进行验证,得到的手臂关节点平均跟踪精度分别为81.4%和84.5%,与现有方法相比有明显提高;此外,通过在VideoPose2.0数据集上的实验,验证了本文提出的对下臂附加采样的算法和手臂特征一致性计算的算法能够有效提高人体姿态关节点的跟踪精度。结论 提出的结合时空信息与深度学习网络的人体姿态跟踪方法能够有效提高人体姿态跟踪的精度,特别是对灵活运动的人体姿态下臂关节点的跟踪精度有显著提高。