92 research outputs found

    Multi-Person Pose Estimation with Local Joint-to-Person Associations

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    Despite of the recent success of neural networks for human pose estimation, current approaches are limited to pose estimation of a single person and cannot handle humans in groups or crowds. In this work, we propose a method that estimates the poses of multiple persons in an image in which a person can be occluded by another person or might be truncated. To this end, we consider multi-person pose estimation as a joint-to-person association problem. We construct a fully connected graph from a set of detected joint candidates in an image and resolve the joint-to-person association and outlier detection using integer linear programming. Since solving joint-to-person association jointly for all persons in an image is an NP-hard problem and even approximations are expensive, we solve the problem locally for each person. On the challenging MPII Human Pose Dataset for multiple persons, our approach achieves the accuracy of a state-of-the-art method, but it is 6,000 to 19,000 times faster.Comment: Accepted to European Conference on Computer Vision (ECCV) Workshops, Crowd Understanding, 201

    Experimental antiproton nuclear stopping power in H2 and D2

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    Data about antiprotons slowing down in gaseous targets at very low energies (E<1 keV) show that the stopping power in D2 is lower than in H2; the right way to explain this behavior seems to be through a nuclear stopping power derived from the classical Rutherford formula

    Milk yield of Comisana ewes fed RP methionine and lysine at two levels of dietary protein content.

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    PADDOCK SHAPE EFFECTS ON SHEEP GRAZING BEHAVIOUR AND EFFICIENCY.

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