Perceiving Systems, Computer Vision

Learning image statistics for Bayesian tracking

2001

Conference Paper

ps


This paper describes a framework for learning probabilistic models of objects and scenes and for exploiting these models for tracking complex, deformable, or articulated objects in image sequences. We focus on the probabilistic tracking of people and learn models of how they appear and move in images. In particular, we learn the likelihood of observing various spatial and temporal filter responses corresponding to edges, ridges, and motion differences given a model of the person. Similarly, we learn probability distributions over filter responses for general scenes that define a likelihood of observing the filter responses for arbitrary backgrounds. We then derive a probabilistic model for tracking that exploits the ratio between the likelihood that image pixels corresponding to the foreground (person) were generated by an actual person or by some unknown background. The paper extends previous work on learning image statistics and combines it with Bayesian tracking using particle filtering. By combining multiple image cues, and by using learned likelihood models, we demonstrate improved robustness and accuracy when tracking complex objects such as people in monocular image sequences with cluttered scenes and a moving camera.

Author(s): Sidenbladh, H. and Black, M. J.
Book Title: Int. Conf. on Computer Vision, ICCV-2001
Volume: II
Pages: 709-716
Year: 2001

Department(s): Perceiving Systems
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Address: Vancouver, BC, USA

Links: pdf

BibTex

@inproceedings{Black:ICCV:2001,
  title = {Learning image statistics for {Bayesian} tracking},
  author = {Sidenbladh, H. and Black, M. J.},
  booktitle = {Int. Conf. on Computer Vision, ICCV-2001},
  volume = {II},
  pages = {709-716},
  address = {Vancouver, BC, USA},
  year = {2001},
  doi = {}
}