Video foreground and background separation method based on cascaded convolutional neural network

A convolutional neural network and foreground-background separation technology, applied in the field of computer vision, can solve problems such as foreground leakage, inability to effectively capture foreground motion information, inaccurate detection of foreground moving objects, etc., to improve learning ability, easy to implement, and program simple effect

Active Publication Date: 2020-08-04
TIANJIN UNIV
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Problems solved by technology

However, there are still some limitations to this type of work
Specifically, they only take one video frame as input, which cannot effectively capture the motio

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  • Video foreground and background separation method based on cascaded convolutional neural network
  • Video foreground and background separation method based on cascaded convolutional neural network
  • Video foreground and background separation method based on cascaded convolutional neural network

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Embodiment Construction

[0047] In order to make up for the deficiencies of the prior art, the present invention proposes a cascaded convolutional neural network incorporating spatio-temporal cues, which consists of two encoder-decoder type sub-networks, namely the foreground detection network (FD network) and the background reconstruction network (BR network). The FD network is used to generate a binary foreground mask, and the BR network uses the output of the FD network and the input video frame to reconstruct the background image. To introduce spatial clues, the present invention takes three consecutive video frames as input. To improve the network applicability, the optical flow maps corresponding to the original video frames are simultaneously input into the FD network as spatial cues. The specific method includes the following steps:

[0048] 1) Establish a training database.

[0049]11) Use the ChangeDetection2014 (abnormal object detection) database, which is a public dataset that contains...

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Abstract

The invention belongs to the field of computer vision, and provides a cascaded convolutional neural network fused with space-time clues, which is used for realizing video foreground and background separation. Therefore, the technical scheme adopted by the invention is as follows that in a video foreground and background separation method based on a cascaded convolutional neural network, two encoder-decoder type sub-networks are used to carry out video foreground and background separation; the two sub-networks are respectively an FD network for foreground detection and a BR network for background reconstruction, the FD network is used for generating a binarized foreground mask, and the BR network reconstructs a background image by using output and input video frames of the FD network; in order to introduce a space clue, three continuous video frames are taken as input; in order to improve the network applicability, the optical flow graph corresponding to the original video frame is usedas a space clue and is input into the FD network at the same time. The method is mainly applied to occasions of video foreground and background separation.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular, relates to a video foreground and background separation method based on a cascaded convolutional neural network. Background technique [0002] Foreground and background separation is a very important basic task in the field of computer vision, which has attracted more and more attention from many researchers. This technology has a wide range of applications, including motion detection, object tracking, behavior recognition, and more. Briefly, the specific task is to extract two complementary components from an input video sequence: a static background and a foreground with moving objects. Over the past decade, many approaches have been proposed to address this problem. The earliest traditional methods, such as Gaussian mixture models, nonparametric models, etc., propose to estimate each pixel independently and classify the pixels as background or foreground. The disadvantage o...

Claims

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Application Information

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IPC IPC(8): G06T7/194G06N3/04
CPCG06T7/194G06T2207/10016G06N3/045
Inventor 杨敬钰师雯
Owner TIANJIN UNIV
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