A Fully Convolutional Encoder–Decoder Spatial–Temporal Network for Real-Time Background Subtraction

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.

Abstract licence: CC BY-NC-ND

32 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.