Detecting moving objects from an image sequence is challenging,
especially when the camera is moving and the background varies significantly
in every frame. In addition, classifying moving objects using
only their appearances creates ambiguities in complex scenes. In this
sense a Markov random field (MRF) approach is proposed incorporating
a stereo vision-based structure-from-motion scheme in order to
robustly detect the moving objects from image sequences. In this
MRF formulation, the new energy terms of a high-order likelihood
and a temporal pairwise potential are added to improve the detection
performance further. The performance of the proposed method is
demonstrated from publicly available datasets.
especially when the camera is moving and the background varies significantly
in every frame. In addition, classifying moving objects using
only their appearances creates ambiguities in complex scenes. In this
sense a Markov random field (MRF) approach is proposed incorporating
a stereo vision-based structure-from-motion scheme in order to
robustly detect the moving objects from image sequences. In this
MRF formulation, the new energy terms of a high-order likelihood
and a temporal pairwise potential are added to improve the detection
performance further. The performance of the proposed method is
demonstrated from publicly available datasets.