Video saliency detection method based on deep network

A deep network and detection method technology, applied in biological neural network models, image data processing, instruments, etc., can solve the problems of inaccurate detection and incomplete detection of algorithms

Active Publication Date: 2020-07-28
HEBEI UNIV OF TECH
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Problems solved by technology

[0006] The technical problem to be solved by the present invention is: provide a video saliency detection method based on a deep network, the method is to use the ResNet50 deep network to get the spatial features, and then extract the time and edge information to jointly obtain the salien

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  • Video saliency detection method based on deep network

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Embodiment 1

[0077] In this embodiment, the salient objects are a cat and a box. The video saliency detection method based on the deep network described in this embodiment, the specific steps are as follows:

[0078] The first step, input video frame I, carry out preprocessing:

[0079] Input a video frame I whose salient target is a cat and a box, unify the size of the video frame to be 473×473 pixels in width and height, and subtract the value of the corresponding channel from each pixel value in the video frame I Mean value, wherein, the mean value of the R channel of each video frame I is 104.00698793, the mean value of the G channel in each video frame I is 116.66876762, and the mean value of the B channel in each video frame I is 122.67891434, so, input to ResNet50 The shape of the video frame I before the deep network is 473×473×3, and the video frame after such preprocessing is recorded as I′, as shown in the following formula (1):

[0080] I'=Resize(I-Mean(R,G,B)) (1),

[0081] ...

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Abstract

The invention discloses a video saliency detection method based on a deep network, and relates to the field of image data processing. The video saliency detection method comprises the following steps:firstly, using the ResNet50 deep network to obtain spatial features, then extracting time and edge information to jointly obtain a saliency prediction result graph, and completing video saliency detection based on the deep network, comprising the following steps: inputting a video frame I, and performing preprocessing; extracting an initial spatial feature map S of the video frame I'; obtaining aspatial feature map Sfinal of five scales; obtaining a feature map F; obtaining a rough space-time saliency map YST and an edge contour map Et of the saliency object; obtaining a final saliency prediction result graph Yfinal; calculating the loss of the input video frame I, and completing the video saliency detection based on the deep network. According to the method, the defects of incomplete salient target detection and inaccurate algorithm detection when foreground and background colors are similar in video saliency detection in the prior art are overcome.

Description

technical field [0001] The technical solution of the present invention relates to the field of image data processing, in particular to a video saliency detection method based on a deep network. Background technique [0002] Video saliency detection aims to extract the regions of most interest to human eyes in consecutive video frames. Specifically, it is one of the key technologies in the field of computer vision to use the computer to simulate the visual attention mechanism of the human eye and extract the area of ​​interest to the human eye from the video frame. [0003] Most of the traditional video saliency detection methods are based on low-level handcrafted features (such as color, texture, etc.), which are typical heuristic methods with slow speed (due to time-consuming optical flow calculation) and low prediction accuracy (due to limited characterization of low-level features). In recent years, deep neural networks have been applied to the field of video saliency d...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06T5/00G06T7/13G06N3/04
CPCG06T5/002G06T7/13G06N3/049G06T2207/10016G06T2207/10024G06T2207/20221G06T2207/20081G06T2207/20084G06V20/46G06V2201/07G06N3/045G06F18/213
Inventor 于明夏斌红刘依郭迎春郝小可朱叶师硕于洋阎刚
Owner HEBEI UNIV OF TECH
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