Joint tracking and video registration by factorial Hidden Markov models

Tracking moving objects from image sequences obtained by a moving camera is a difficult problem since there exists apparent motion of the static background. It becomes more difficult when the camera motion between the consecutive frames is very large. Traditionally, registration is applied before tracking to compensate for the camera motion using parametric motion models. At the same time, the tracking result highly depends on the performance of registration. This raises problems when there are big moving objects in the scene and the registration algorithm is prone to fail, since the tracker easily drifts away when poor registration results occur. In this paper, we tackle this problem by registering the frames and tracking the moving objects simultaneously within the factorial Hidden Markov Model framework using particle filters. Under this framework, tracking and registration are not working separately, but mutually benefit each other by interacting. Particles are drawn to provid...

• Type: Conference
• Year: 2008
• Where: ICASSP
• Authors: Xue Mei, Fatih Murat Porikli
• Keywords: Big Moving Objects, Camera Motion, Geometric Transformation Parameters, ICASSP 2008, Signal Processing