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Human body parts segmentation via stacked and multi-task learning

Human Body Segmentation in RGB images has been a core problem on the Computer Vision field since its early beginnings. In this particular problem, the goal is to provide with a complete segmentation of the human/s body parts appearing in an image, discriminating the human body from the rest of the image. It is a very challenging area since it has to face many handicaps related to high variability in data such as lighting conditions, cluttering, clothes, appearance, background, point of view and number of human body parts, among others. Even so, it has become one of the areas of research because of its capabilities in real applications (i.e.surveillance, medical imaging, sign language, interactive virtual reality systems). Hand-crafted methods covered traditional methods such as simple matching templates, deformable models, pictorial structures with tree and loopy models and discriminative ensembles learning. These approaches took researchers to point out rigorous studies to constraint the problem either by kinematic structure reasons or variability in poses/samples. However, with the appearance of deep-based methods, the traditional pipelines and methods have changed to use Deep Convolutional Neural Networks in its different variations merely. As a result, deep-based methods have been surpassing by a large margin the hand-crafted methods getting the researchers to focus on the latter methods and in their combination with traditional ones. The writing of this thesis coincides with the paradigm shift; therefore, it is evidenced into two distinctive blocks. In the first block, we focus on a novel dataset in order to extend the state-of-the-art in human pose estimation and body segmentation. Next, we present a novel two-stage approach for human body part segmentation. We propose to use a cascade of classifiers as body parts detectors combining their outputs in an ErrorCorrecting Output Codes framework. Once we obtain the body pose, we apply Graph Cut segmentation optimization. Then, we use HOG features to describe the dataset and train SVM classifiers combined with the ECOC framework to feed a body part segmentation Graph Cut approach. Moreover, we face full body segmentation, but differently, we present a novel twostage human body segmentation method based on the discriminative Multi-Scale Stacked