We present an adaptive framework for condensation algorithms in the context of human-face tracking. We attack the face tracking problem by making factored sampling more efficient a...
Yui Man Lui, J. Ross Beveridge, L. Darrell Whitley
We derive a probabilistic framework for robust, real-time, visual tracking of previously unseen objects from a moving camera. The tracking problem is handled using a bag-of-pixels ...
Online learned tracking is widely used for it’s adaptive ability to handle appearance changes. However, it introduces potential drifting problems due to the accumulation of erro...
In this paper, we present a probabilistic algorithm for visual tracking that incorporates robust template matching and incremental subspace update. There are two template matching...
We describe an online approach to learn non-linear motion patterns and robust appearance models for multi-target tracking in a tracklet association framework. Unlike most previous...