ANR DEEPVISION: Seeing and understanding humans with deep structured models

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Advances in computer vision have enabled applications that seemed impossible some years ago, e.g. gesture recognition in real time, face detection on mobile devices and 3d mapping of environments. Machine learning is a major driving force behind this development. The vast amount of data corresponding to visual information, as well as its inherently large variability due to different viewpoints, shapes, etc. make learning an appealing approach. However, the current state of the art suffers from limitations. Structural relationships put many realistic situations out of reach of commercial applications, such as person-person and person-object interactions, long-term dynamical behaviour, and the recognition of highly deformable and articulated objects. We propose to address these challenges by creating deep structured models, combining the advantages of deep learning, a powerful family of models and algorithms, with structured models, which are well-suited for modeling complex data.