Two-granularity tracking: mediating trajectory and detection graphs for tracking under occlusions
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Finding people using scale, rotation and articulation invariant matching
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Qualitative pose estimation by discriminative deformable part models
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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We present an approach for automatic 3D human pose reconstruction from monocular images, based on a discriminative formulation with latent segmentation inputs. We advanced the field of structured prediction and human pose reconstruction on several fronts. First, by working with a pool of figure-ground segment hypotheses, the prediction problem is formulated in terms of combined learning and inference over segment hypotheses and 3D human articular configurations. Beside constructing tractable formulations for the combined segment selection and pose estimation problem, we propose new augmented kernels that can better encode complex dependencies between output variables. Furthermore, we provide primal linear re-formulations based on Fourier kernel approximations, in order to scale-up the non-linear latent structured prediction methodology. The proposed models are shown to be competitive in the HumanEva benchmark and are also illustrated in a clip collected from a Hollywood movie, where the model can infer human poses from monocular images captured in complex environments.