Articulated pose estimation with flexible mixtures-of-parts
We describe a method for articulated human detection and human pose estimation in static images based on
a new representation of deformable part models. Rather than modeling articulation using a family of warped (rotated and
foreshortened) templates, we use a mixture of small, non-oriented parts. We describe a general, flexible mixture model that
jointly captures spatial relations between part locations and co-occurrence relations between part mixtures, augmenting standard
pictorial structure models that encode just spatial relations. Our models have several notable properties: (1) they efficiently model
articulation by sharing computation across similar warps (2) they efficiently model an exponentially-large set of global mixtures
through composition of local mixtures and (3) they capture the dependency of global geometry on local appearance (parts
look different at different locations). When relations are tree-structured, our models can be efficiently optimized with dynamic
programming. We learn all parameters, including local appearances, spatial relations, and co-occurrence relations (which encode
local rigidity) with a structured SVM solver. Because our model is efficient enough to be used as a detector that searches over
scales and image locations, we introduce novel criteria for evaluating pose estimation and human detection, both separately
and jointly. We show that currently-used evaluation criteria may conflate these two issues. Most previous approaches model
limbs with rigid and articulated templates that are trained independently of each other, while we present an extensive diagnostic
evaluation that suggests that flexible structure and joint training are crucial for strong performance. We present experimental
results on standard benchmarks that suggest our approach is the state-of-the-art system for pose estimation, improving past
work on the challenging Parse and Buffy datasets, while being orders of magnitude faster.
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Text Reference
Yi Yang and Deva Ramanan. Articulated pose estimation with flexible mixtures-of-parts. IEEE TPAMI, 2013.BibTeX Reference
@ARTICLE{YangR_TPAMI_2013,author = "Yang, Yi and Ramanan, Deva",
title = "Articulated pose estimation with flexible mixtures-of-parts",
journal = "IEEE TPAMI",
year = "2013",
tag = "grouping"
}