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A Fully Convolutional Encoder-Decoder Spatial-Temporal Network for Real-Time Background Subtraction
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DOI：10.1109/ACCESS.2019.2925913.png)
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Abstract
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Background subtraction is described as the task of distinguishing pixels into moving objects and the background in a frame. In this paper, we propose a fully convolutional encoder-decoder spatial-temporal network (FCESNet) to achieve real-time background subtraction. In the proposed many-to-many architecture method encoded features of consecutive frames are fed into a spatial-temporal information transmission (STIT) module to capture the spatial-temporal correlation in the frame sequence, and then a decoder is designed to output the subtraction results of all frames. A patch-based training method is designed to increase the practicability and flexibility of the proposed method. The experiments over CDNet2014 have shown that the proposed method could achieve state-of-the-art performance. The proposed method is proved to be able to achieve real-time background subtraction.
Keywords:
Background subtraction
many-to-many
real-time
scene compatibility
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