Thus, the moving objects can be naturally separated by a tensor-based decomposition model which formulates the static background by a unidirectional low-rank tensor, learns the dynamic clutter by a two-stream neural network, and constrains moving objects with spatiotemporal continuity.
Then the dynamic clutter can be learned through object-free video data due to their self-similarity across time and space. In order to improve the generalization ability of low-rank based moving object detectors, we suggest adding an explicit dynamic clutter component in the decomposition framework with realistic dynamic background modeling. The main reason is that dynamic clutters, e.g., swaying leaves and rippers, are easy to mix up with moving objects in the decomposition model which simply classify the sparse noise as foregrounds.
While low-rank based approaches have achieved impressive success in the MOD task, their performance remains limited on dynamics background scenes. Moving Object Detection (MOD) aims at extracting foreground moving objects in videos from static cameras.
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