  Change detection is a fundamental task in computer vision. Despite
significant advances have been made, most of the change detection methods fail
to work well in challenging scenes due to ubiquitous noise and interferences.
Nowadays, post-processing methods (e.g. MRF, and CRF) aiming to enhance the
binary change detection results still fall short of the requirements on
universality for distinctive scenes, applicability for different types of
detection methods, accuracy, and real-time performance. Inspired by the nature
of image filtering, which separates noise from pixel observations and recovers
the real structure of patches, we consider utilizing image filters to enhance
the detection masks. In this paper, we present an integrated filter which
comprises a weighted local guided image filter and a weighted spatiotemporal
tree filter. The spatiotemporal tree filter leverages the global spatiotemporal
information of adjacent video frames and meanwhile the guided filter carries
out local window filtering of pixels, for enhancing the coarse change detection
masks. The main contributions are three: (i) the proposed filter can make full
use of the information of the same object in consecutive frames to improve its
current detection mask by computations on a spatiotemporal minimum spanning
tree; (ii) the integrated filter possesses both advantages of local filtering
and global filtering; it not only has good edge-preserving property but also
can handle heavily textured and colorful foreground regions; and (iii) Unlike
some popular enhancement methods (MRF, and CRF) that require either a priori
background probabilities or a posteriori foreground probabilities for every
pixel to improve the coarse detection masks, our method is a versatile
enhancement filter that can be applied after many different types of change
detection methods, and is particularly suitable for video sequences.
