Abstract
We present a discriminative deformable part model for the recovery of qualitative pose, inferring coarse pose labels (e.g., left, front-right, back), a task which we expect to be more robust to common confounding factors that hinder the inference of exact 2D or 3D joint locations. Our approach automatically selects parts that are predictive of qualitative pose and trains their appearance and deformation costs to best discriminate between qualitative poses. Unlike previous approaches, our parts are both selected and trained to improve qualitative pose discrimination and are shared by all the qualitative pose models. This leads to both increased accuracy and higher efficiency, since fewer parts models are evaluated for each image. In comparisons with two state-of-the-art approaches on a public dataset, our model shows superior performance.
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Lee, H., Morariu, V.I., Davis, L.S. (2013). Qualitative Pose Estimation by Discriminative Deformable Part Models. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7725. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37444-9_57
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DOI: https://doi.org/10.1007/978-3-642-37444-9_57
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