Merging pose estimates across space and time
Abstract
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.
Additional Information
© 2013 The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms. This work is funded by the Gordon and Betty Moore Foundation and ONR MURI Grant N00014-10-1-0933.Attached Files
Published - tracking_bmvc.pdf
Supplemental Material - material0058.zip
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Additional details
- Eprint ID
- 41565
- Resolver ID
- CaltechAUTHORS:20130930-145804309
- Gordon and Betty Moore Foundation
- N00014-10-1-0933
- Office of Naval Research (ONR)
- Created
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2013-09-30Created from EPrint's datestamp field
- Updated
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2021-11-10Created from EPrint's last_modified field