## 1 Introduction  
Human segmentation in uncontrolled environments is a hard task because of the constant changes produced in natural scenes: illumination changes, moving objects, changes in the point of view, occlusions, just to mention a few. Because of the nature of the problem, a common way to proceed is to discard most part of the image so that the analysis can be performed on a reduced set of small candidate regions. In [1], the authors propose a full-body detector based on a cascade of classifiers [2] using HOG features. This methodology is currently being used in several works related to the pedestrian detection problem [3]. GrabCut [4] has also shown high robustness in Computer Vision segmentation problems, defining the pixels of the image as nodes of a graph and extracting foreground pixels via iterated Graph Cut optimization. This methodology has been applied to the problem of human body segmentation with high success [5,6]. In the case of working with sequences of images, this optimization problem can also be considered to have temporal coherence. In the work of [7], the authors extended the Gaussian Mixture Model (GMM) of GrabCut algorithm so that the color space is complemented with the derivative in time of pixel intensities in order to include temporal information in the segmentation optimization process. However, the main problem of that method is that moving pixels corresponds to the boundaries between foreground and background regions, and thus, there is no clear discrimination.  
Once a region of interest is determined, pose is often recovered by the determination of the body limbs together with their spatial coherence (also with temporal coherence in case of image sequences). Most of these approaches are probabilistic, and features are usually based on edges or \"appearance\". In [8], the author propose a probabilistic approach for limb detection based on edge learning complemented with color information. The image of probabilities is then formulated in a Conditional Random Field (CRF) scheme and optimized using belief propagation. This work has obtained robust results and has been extended by other authors including local GrabCut segmentation and temporal refinement of the CRF model [5,6].  
In this paper, we propose a full-automatic Spatio-Temporal GrabCut human segmentation methodology, which benefits from the combination of tracking and segmentation. First, subjects are detected by means of a HOG-based cascade of classifiers. Face detection and skin color model are used to define a set of seeds used to initialize GrabCut algorithm. Spatial information is taken into account by means of Mean Shift clustering, whereas temporal information is considered taking into account the pixel probability membership to an historical of Gaussian Mixture Models. Moreover, the methodology is combined with Shape and Active Appearance Models (AAM) to define three different meshes of the face, one near frontal view, and the other ones near lateral views. Temporal coherence and fitting cost are considered in conjunction with GrabCut segmentation to allow a smooth and robust face fitting in video sequences. Finally, the limb detection and a CRF model are applied on the obtained segmentation, showing high robustness capturing body limbs due to the accurate human segmentation. The main limitation of our approach is that it depends on a correct detection of the person and his/her face, in order to get the desired result. In order to test the proposed methodology, we use public datasets and present a new Human Limb dataset useful for human segmentation, limb detection, and pose recovery purposes.  
The rest of the paper is organized as follows: Section 2 describes the proposed methodology, presenting the spatio-temporal GrabCut segmentation, the AAM for face fitting, and the pose recovery methodology. Experimental results on public and novel datasets are performed in Section 3. Finally, Section 4 concludes the paper.