Estimating multi-person poses using greedy part assignment

A method for estimating multi-person poses, comprising:

• estimating, via the processor, a number of people based on a significant head count of a plurality of received refined part detections;
• clustering, via the processor, the refined part detections based on the estimated number of people to generate clustered part detections;
• selecting, via the processor, a number of candidate person clusters for each clustered part detection based on proximity to the clustered part detection;
• calculating, via the processor, a cluster affinity score for each combination of candidate person cluster and clustered part detection, and greedily sequentially assigning, via the processor, each clustered part detection to a candidate person cluster based on the cluster affinity score to generate person clusters;
• and
• generating, via the processor, a pose for each person cluster.

At least one computer readable medium for estimating multi-person poses having instructions stored therein that, in response to being executed on a computing device, cause the computing device to:

• receive body part candidates and
• refine the body part candidates to generate refined part detections;
• estimate a number of people based on a significant head count of the refined part detections;
• cluster the refined part detections based on the estimated number of people to generate clustered part detections;
• select a number of candidate person clusters for each clustered part detection based on proximity to the clustered part detection;
• calculate a cluster affinity score for each combination of candidate person cluster and clustered part detection;
• greedily sequentially assign each clustered part detection to a candidate person cluster based on the cluster affinity score to generate person clusters;
• and
• generate a pose for each person cluster.