An optimization based framework for human pose estimation in monocular videos

Abstract

Human pose estimation using monocular vision is a challenging problem in computer vision. Past work has focused on developing efficient inference algorithms and probabilistic prior models based on captured kinematic/dynamic measurements. However, such algorithms face challenges in generalization beyond the learned dataset. In this work, we propose a model-based generative approach for estimating the human pose solely from uncalibrated monocular video in unconstrained environments without any prior learning on motion capture/image annotation data. We propose a novel Product of Heading Experts (PoHE) based generalized heading estimation framework by probabilistically-merging heading outputs (probabilistic/ non-probabilistic) from time varying number of estimators to bootstrap a synergistically integrated probabilistic-deterministic sequential optimization framework for robustly estimating human pose. Novel pixel-distance based performance measures are developed to penalize false human detections and ensure identity-maintained human tracking. We tested our framework with varied inputs (silhouette and bounding boxes) to evaluate, compare and benchmark it against ground-truth data (collected using our human annotation tool) for 52 video vignettes in the publicly available DARPA Mind's Eye Year I dataset. Results show robust pose estimates on this challenging dataset of highly diverse activities. © 2012 Springer-Verlag.