  Human pose-estimation in a multi-person image involves detection of various
body parts and grouping them into individual person clusters. While the former
task is challenging due to mutual occlusions, the combinatorial complexity of
the latter task is very high. We propose a greedy part assignment algorithm
that exploits the inherent structure of the human body to achieve a lower
complexity, compared to any of the prior published works. This is accomplished
by (i) reducing the number of part-candidates using the estimated number of
people in the image, (ii) doing a greedy sequential assignment of part-classes,
following the kinematic chain from head to ankle (iii) doing a greedy
assignment of parts in each part-class set, to person-clusters (iv) limiting
the candidate person clusters to the most proximal clusters using human
anthropometric data and (v) using only a specific subset of pre-assigned parts
for establishing pairwise structural constraints. We show that, these steps
result in a sparse body parts relationship graph and reduces the complexity. We
also propose methods for improving the accuracy of pose-estimation by (i)
spawning person-clusters from any unassigned significant body part and (ii)
suppressing hallucinated parts. On the MPII multi-person pose database,
pose-estimation using the proposed method takes only 0.14 seconds per image. We
show that, our proposed algorithm, by using a large spatial and structural
context, achieves the state-of-the-art accuracy on both MPII and WAF
multi-person pose datasets, demonstrating the robustness of our approach.
