Name
Abstract | ||
|---|---|---|
Despite many attempts in the last few years, automatic analysis of social scenes captured by wide-angle camera networks remains a very challenging task due to the low resolution of targets, background clutter and frequent and persistent occlusions. In this paper, we present a novel framework for jointly estimating (i) head, body orientations of targets and (ii) conversational groups called from social scenes. In contrast to prior works that have (a) exploited the limited range of head and body orientations to jointly learn both, or (b) employed the mutual head (but not body) pose of interactors for deducing F-formations, we propose a weakly-supervised learning algorithm for joint inference. Our algorithm employs body pose as the primary cue for F-formation estimation, and an alternating optimization strategy is proposed to iteratively refine F-formation and pose estimates. We demonstrate the increased efficacy of joint inference over the state-of-the-art via extensive experiments on three social datasets. |
Year | DOI | Venue |
|---|---|---|
2018 | https://doi.org/10.1007/s11263-017-1026-6 | International Journal of Computer Vision |
Keywords | Field | DocType |
Head and body pose estimation,F-formation estimation,Semi-supervised learning,Convex optimization,Conversational groups,Video surveillance | Computer vision,Semi-supervised learning,Clutter,Inference,Computer science,3D pose estimation,Camera network,Artificial intelligence,Articulated body pose estimation,Convex optimization,Machine learning | Journal |
Volume | Issue | ISSN |
126 | 2-4 | 0920-5691 |
Citations | PageRank | References |
3 | 0.39 | 40 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jagannadan Varadarajan | 1 | 176 | 11.47 |
| Ramanathan Subramanian | 2 | 461 | 22.16 |
| Samuel Rota Bulò | 3 | 564 | 33.69 |
| Narendra Ahuja | 4 | 7726 | 956.74 |
| Oswald Lanz | 5 | 462 | 33.34 |
| Elisa Ricci 0002 | 6 | 1393 | 73.75 |