Learning Image Statistics for Bayesian Tracking

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 filt...

• Type: Conference
• Year: 2001
• Where: ICCV
• Authors: Hedvig Sidenbladh, Michael J. Black
• Keywords: Computer Vision, Filter Responses, ICCV 2001, Likelihood Models, Monocular Image Sequences, Probabilistic Tracking, Temporal Filter Responses