Abstract
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.
Highlights
Change detection aims at segmenting moving objects from images or throughout a video sequence
Since the Millennium, substantial research achievements have been emerging rapidly, e.g., Kernel Density Estimation (KDE) based foreground detection [5], unsupervised clustering-inspired codebook algorithm [6], foreground detection based on random strategies [7], the robust principal component analysis (RPCA) approach in which the background is modeled by a low-rank subspace and the foreground components are regarded as a noise component [8]–[10], and video object segmentation via deep learning [11]–[13]
We present an integrated filter which comprises a weighted local guided image filter and a weighted spatiotemporal tree filter for enhancing the coarse change detection masks
Summary
Change detection aims at segmenting moving objects from images or throughout a video sequence. It is a fundamental task in computer vision, and serves in important applications including intelligent security, national defense, intelligent transportation, etc. Since the Millennium, substantial research achievements have been emerging rapidly, e.g., Kernel Density Estimation (KDE) based foreground detection [5], unsupervised clustering-inspired codebook algorithm [6], foreground detection based on random strategies [7], the robust principal component analysis (RPCA) approach in which the background is modeled by a low-rank subspace and the foreground components are regarded as a noise component [8]–[10], and video object segmentation via deep learning [11]–[13]
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