A probabilistic framework for joint segmentation and tracking

Most tracking algorithms implicitly apply a coarse segmentation of each target object using a simple mask such as a rectangle or an ellipse. Although convenient, such coarse segmentation results in several problems in tracking—drift, switching of targets, poor target localization, to name a few—since it inherently includes extra non-target pixels if the mask is larger than the target or excludes some portion of target pixels if the mask is smaller than the target. In this paper, we propose a novel probabilistic framework for jointly solving segmentation and tracking. Starting from a joint Gaussian distribution over all the pixels, candidate target locations are evaluated by first computing a pixel-level segmentation and then explicitly including this segmentation in the probability model. The segmentation is also used to incrementally update the probability model based on a modified probabilistic principal component analysis (PPCA). Our experimental results show that the propose...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Chad Aeschliman, Johnny Park, Avinash C. Kak
• Keywords: Coarse Segmentation, Computer Vision, CVPR 2010, Most Tracking Algorithms, Probability Model