Abstract. The emergence of affordable depth cameras has enabled significant advances in human segmentation and pose estimation in recent years. While it leads to impressive results in many tasks, the use of infrared cameras have their drawbacks, in particular the fact that they don't work in direct sunlight. One alternative is to use a stereo pair of cameras to produce a disparity space image. In this work, we propose a robust method of using a disparity space image to create a prior for human segmentation. This new prior leads to greatly improved segmentation results; it can be applied to any task where a stereo pair of cameras is available, and segmentation results are desired. As an application, we show how the prior can be inserted into a dual decomposition formulation for stereo, segmentation and human pose estimation.
Abstract. Many models have been proposed to estimate human pose and segmentation by leveraging information from several sources. A standard approach is to formulate it in a dual decomposition framework. However, these models generally suffer from the problem of high computational complexity. In this work, we propose PoseField, a new highly efficient filter-based mean-field inference approach for jointly estimating human segmentation, pose, per-pixel body parts, and depth given stereo pairs of images. We extensively evaluate the efficiency and accuracy offered by our approach on H2View [1], and Buffy [2] datasets. We achieve 20 to 70 times speedup compared to the current state-of-the-art methods, as well as achieving better accuracy in all these cases.
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