Merging Pose Estimates Across Space and Time

Numerous `non-maximum suppression’ (NMS) post-processing schemes have been proposed for merging multiple independent object detections. We propose a generalization of NMS beyond bounding boxes to merge multiple pose estimates in a single frame. The final estimates are centroids rather than medoids as in standard NMS, thus being more accurate than any of the individual candidates. Using the same mathematical framework, we extend our approach to the multi-frame setting, merging multiple independent pose estimates across space and time and outputting both the number and pose of the objects present in a scene. Our approach sidesteps many of the inherent challenges associated with full tracking (e.g. objects entering/leaving a scene, extended periods of occlusion, etc.). We show its versatility by applying it to two distinct state-of-the-art pose estimation algorithms in three domains: human bodies, faces and mice. Our approach improves both detection accuracy (by helping disambiguate correspondences) as well as pose estimation quality and is computationally efficient.