Measurement Function Design for Visual Tracking Applications

Extracting human postural information from video sequences has proved a difficult research question. The most successful approaches to date have been based on particle filtering, whereby the underlying probability distribution is approximated by a set of particles. The shape of the underlying observational probability distribution plays a significant role in determining the success, both accuracy and efficiency, of any visual tracker. In this paper we compare approaches used by other authors and present a cost path approach which is commonly used in image segmentation problems, however is currently not widely used in tracking applications.

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Andrew W. B. Smith, Brian C. Lovell
• Keywords: Computer Vision, Cost Path Approach, ICPR 2006, Image Segmentation Problems, Observational Probability Distribution