A Greedy Part Assignment Algorithm for Real-time Multi-person 2D Pose Estimation

Understand

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.