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 arc...

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Bibliographic Details
Published in:IEEE access Vol. 7; pp. 85949 - 85958
Main Authors: Qiu, Mingkai, Li, Xiying
Format: Journal Article
Language:English
Published: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Background subtraction
Coders
Decoding
Deep learning
Encoders-Decoders
Feature extraction
Frames (data processing)
Information processing
many-to-many
Real time
Real-time systems
scene compatibility
Subtraction
Training
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2925913