2 research outputs found
CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark
Multi-person pose estimation is fundamental to many computer vision tasks and
has made significant progress in recent years. However, few previous methods
explored the problem of pose estimation in crowded scenes while it remains
challenging and inevitable in many scenarios. Moreover, current benchmarks
cannot provide an appropriate evaluation for such cases. In this paper, we
propose a novel and efficient method to tackle the problem of pose estimation
in the crowd and a new dataset to better evaluate algorithms. Our model
consists of two key components: joint-candidate single person pose estimation
(SPPE) and global maximum joints association. With multi-peak prediction for
each joint and global association using graph model, our method is robust to
inevitable interference in crowded scenes and very efficient in inference. The
proposed method surpasses the state-of-the-art methods on CrowdPose dataset by
5.2 mAP and results on MSCOCO dataset demonstrate the generalization ability of
our method. Source code and dataset will be made publicly available
Scaling Bayesian Probabilistic Record Linkage with Post-Hoc Blocking: An Application to the California Great Registers
Probabilistic record linkage (PRL) is the process of determining which
records in two databases correspond to the same underlying entity in the
absence of a unique identifier. Bayesian solutions to this problem provide a
powerful mechanism for propagating uncertainty due to uncertain links between
records (via the posterior distribution). However, computational considerations
severely limit the practical applicability of existing Bayesian approaches. We
propose a new computational approach, providing both a fast algorithm for
deriving point estimates of the linkage structure that properly account for
one-to-one matching and a restricted MCMC algorithm that samples from an
approximate posterior distribution. Our advances make it possible to perform
Bayesian PRL for larger problems, and to assess the sensitivity of results to
varying prior specifications. We demonstrate the methods on a subset of an
OCR'd dataset, the California Great Registers, a collection of 57 million voter
registrations from 1900 to 1968 that comprise the only panel data set of party
registration collected before the advent of scientific surveys.Comment: 42 pages with appendices, 7 figures, 20 page supplemen